Abstract
Background
Gene-based nutrition recommendations have emerged as a strategy for weight management, but evidence of their added value over standard advice remains inconclusive. This randomized controlled trial evaluated the effects of MyGeneMyDiet®, a genotype-informed lifestyle program, compared with standard recommendations on anthropometric, biochemical, and dietary outcomes in Filipino adults with overweight and obesity over 12 months.
Methods
In this randomized controlled trial, participants received either MyGeneMyDiet® or standard recommendation (control). Both groups underwent regular nutrition counseling during the active phase (months 0–6) before transitioning to an inactive phase (free-living conditions, months 6–12). Primary outcomes included weight, BMI, waist circumference, and body fat percentage; secondary outcomes were dietary intake and biochemical markers. Analyses were conducted according to randomized group assignment. Primary analyses used available case-data at each timepoint, with paired t-tests for within-group comparisons and ANCOVA for between-group differences. Sensitivity analyses used Last Observation Carried Forward (LOCF) and Inverse Probability of Attrition Weighting (IPAW) to address loss-to-follow-up.
Results
Of the 136 screened, 52 initiated the intervention (MyGeneMyDiet®, n = 29; standard recommendation, n = 23), and 27 completed the 12-month follow-up (MyGeneMyDiet®, n = 15; standard recommendation, n = 12). Both groups lost weight over 12 months, with no evidence of meaningful between-group differences. During the 6-month active phase, baseline-adjusted analyses showed no significant between-group differences in weight (-0.36 kg [95% CI: -1.77, 1.04]), BMI (0.11 kg/m2 [95% CI: -0.51, 0.73]), waist circumference (-0.27 cm [95% CI: -2.23, 1.69]), or body fat percentage (0.92% [95% CI: -0.86, 1.05]). These trends persisted through 12 months. Both groups reported reductions in energy intake; within-group decreases in energy and macronutrients were observed in the MyGeneMyDiet® arm at month 12, but these did not translate into superior anthropometric or metabolic outcomes.
Conclusions
We found no evidence of meaningful differences between gene-based and standard recommendations in anthropometric or metabolic outcomes over 12 months. While genotype-informed counseling was associated with reported reductions in dietary intake at some timepoints, these changes did not translate into superior clinical outcomes. These findings suggest that genetic tailoring alone may not enhance long-term weight management beyond standard counseling but offer insight into the feasibility of integrating gene-based recommendations in low- and middle-income country settings.
Clinical Trial registration
clinicaltrials.gov, NCT05098899 (https://clinicaltrials.gov/study/NCT05098899); registered 20 October 2021.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12263-026-00794-z.
Keywords: Gene-based nutrition, Obesity, Personalized nutrition, Nutrigenomics, RCT, Weight loss
Introduction
Genetic variation contributes to inter-individual differences in both susceptibility to obesity and response to weight-loss interventions [1–3]. Variants in genes such as the fat mass and obesity-associated (FTO) gene influence appetite and energy balance, with the rs9939609 polymorphism conferring increased obesity risk that may be offset by higher levels of physical activity [4–10]. The uncoupling protein 1 (UCP1) rs1800592 has been linked to altered thermogenesis and reduced response to energy restriction [11–15] while the transcription factor 7-like (TCF7L2) rs7903146 – widely studied in relation to type 2 diabetes, has also been associated with obesity, impaired glucose regulation, and dyslipidemia [16–21]. Evidence further suggests that dietary factors such as saturated fat intake may exacerbate metabolic risk in carriers of TCF7L2 risk alleles [18].
These variants (FTO rs9939609, UCP1 rs1800592, and TCF7L2 rs7903146) provide a biologically plausible and behaviorally actionable basis for tailoring lifestyle recommendations, because their associated risks can be targeted through physical activity, caloric restriction, and dietary fat modification [11, 17, 22–24]. They were therefore selected as the foundation of the MyGeneMyDiet® approach, in which physical activity prescriptions, daily energy targets, and fat-intake goals are customized according to each participant’s genotype, in contrast to standard-weight loss recommendations that are based solely on clinical and lifestyle assessments without genetic tailoring. While gene-diet interaction studies continue to expand [25, 26], evidence on the effectiveness of gene-based recommendations remain inconclusive: some trials report no added benefit beyond standard approaches [26, 27], while others suggest improvements in weight, dietary behavior, or lifestyle modification when advice is personalized to genotype [28–31].
Most prior work has been limited by short follow-up periods, modest sample sizes, and inconsistent reporting of adherence [32–36]. In addition, evidence from low- and middle-income countries (LMICs) is virtually absent, limiting the generalizability of gene-based interventions to diverse settings. Furthermore, the sustainability of behavior change once structured support is withdrawn has rarely been assessed [37–39].
Despite increasing interest in genotype-informed dietary advice, evidence from randomized trials in resource-constrained settings remains limited, particularly regarding whether a small, biologically plausible SNP panel can produce clinically meaningful advantages over standard weight-loss counseling. We therefore conducted a 12-month randomized controlled trial comparing MyGeneMyDiet® gene-based recommendations with standard weight-loss recommendations in overweight and obese adults. We hypothesized that gene-based recommendations would lead to greater improvements in anthropometric outcomes and metabolic markers compared with standard recommendations. The primary outcomes were changes in weight, BMI, waist circumference, and body fat percentage; secondary outcomes included changes in dietary intake (energy and macronutrients) and biochemical markers (lipid and glycated hemoglobin (HbA1c)).
Materials and methods
Study design
This was a 12-month, parallel-group, single-blind randomized controlled trial evaluating the effects of gene-based nutrition recommendations on anthropometric, biochemical, and dietary outcomes in Filipino adults with overweight and obesity (ClinicalTrials.gov identifier: NCT05098899; date of registration: 20 October 2021). Assessments were conducted at baseline (month 0) and at months 3, 6, 9, and 12. The intervention comprised two phases: an active phase (months 0–6), during which participants received regular nutrition counseling and monitoring. During the inactive phase (months 6–12), participants returned for follow-up visits at months 9 and 12 primarily for outcome assessment (anthropometry and blood sampling). No additional individualized nutrition counseling, reinforcement of recommendations, or feedback on progress was provided during this phase beyond standard visit procedures.
The trial followed a pre-specified protocol [40] that detailed the design, intervention, and outcome assessments. No interim analysis was performed, no stopping rules were applied, and the study was completed as planned without early termination. The study adhered to the Declaration of Helsinki and received ethical approval from the Food and Nutrition Research Institute (FNRI) Institutional Ethics Review Committee (FIERC-2021-001). Written informed consent was obtained from all participants prior to the study procedures. Participants signed the consent form electronically and submitted a scanned/electronic copy via email to the official study email address.
A full description of the study design and intervention protocol has been published previously [40] and is briefly summarized here.
Study participants
Participants were recruited from Metro Manila and nearby provinces between December 2021 and November 2022 through invitation letters, social media postings, and snowball sampling. Orientation sessions were conducted online, and informed consent forms were emailed to potentially eligible participants following these sessions.
Eligible participants were adults aged 19–59 years with overweight or obesity (BMI 25–40 kg/m2) who carried at least one risk allele in the selected single nucleotide polymorphisms (SNPs): FTO rs9939609 (A allele), TCF7L2 rs7903146 (T allele), or UCP1 rs1800592 (G allele). This design choice was intended to capture the real-world context in which individuals may carry one or multiple risk alleles, allowing us to evaluate the feasibility and effectiveness of gene-specific recommendations while maintaining recruitment feasibility. A complete list of inclusion and exclusion criteria is provided in Supplement: Additional Table 1.
Screening and data collection protocols
All in-person screening and data collection procedures were conducted at the Food and Nutrition Research Institute (FNRI) in Taguig City, Metro Manila, between December 2021 and November 2022, following national and local COVID-19 guidelines. Prior to each session, participants and research staff completed an online health declaration, and those recently exposed to COVID-19 cases followed prescribed isolation protocols. On the day of assessment, all individuals underwent temperature checks, hand sanitation, and rapid antigen testing.
Data collection was organized into five stations: (1) COVID-19 testing, (2) registration, (3) anthropometric measurements, (4) blood collection, and (5) physician consultation. To minimize contact, participants spent ≤ 15 min per station and adhered to strict queueing protocols. Both participants and staff were monitored for symptoms for 14 days after each session. These protocols were consistently applied at baseline and follow-up visits (months 3, 6, 9, and 12), with adjustments as required by changes in Metro Manila’s quarantine classifications.
Genetic counseling sessions
All participants received genetic counseling prior to randomization into either the intervention or control arm. Counseling was delivered virtually in a two-part, interactive format consisting of pre- and post-genetic test sessions.
The pre-genetic test counseling session, conducted a few days after DNA sample collection and before genotyping, provided education on basic genetics, including genes, chromosomes, genetic variants, and gene-environment interactions, as well as an overview of the testing procedures. The counselor also discussed the potential emotional and psychological implications of receiving genetic test results.
The post-genetic test counseling session, held approximately two weeks later when genotyping results became available, focused on the disclosure of each participant’s genotype results for FTO, TCF7L2, and UCP1, and on the provision of psychosocial support to help participants understand the potential implications of these variants for weight management and lifestyle modification.
Both the intervention and standard recommendation groups received genetic counseling and disclosure of genotype results. The distinction between trial arms lay in how these results were subsequently used: in the intervention arm, genotype information informed tailored dietary and lifestyle recommendations (MyGeneMyDiet®), while in the standard recommendation arm, participants received standard weight management advice without reference to the disclosed genotypes.
Sample size calculation
The required sample size was estimated based on the primary outcome of achieving a ≥ 5% reduction in baseline weight after 6 months of intervention. Assuming a standard deviation of 0.25 for the primary outcome [41], 80% power, and a two-sided significant level of 0.05, we calculated that 52 participants (26 per group) would be required. To account for an anticipated 20% attrition, the target enrollment was increased to 62 participants (31 per group). Although this target as achieved at randomization, the final number of participants who commenced the trial at baseline was slightly lower due to pre-baseline withdrawals.
Randomization and blinding
Participants were randomized in a 1:1 ratio to either the MyGeneMyDiet® intervention or the standard recommendation arm. The randomization sequence was computer-generated using Random Allocation Software with variable block sizes of 4, 6, and 8. To maintain balance across groups, stratification was applied by body mass index (BMI) category according to World Health Organization (WHO) criteria: overweight (25–30 kg/m²) and obese (> 30 kg/m²).
The allocation sequence was prepared by two research staff members independent of participant recruitment, counseling, and outcome assessment. To ensure allocation concealment, 66 sequentially numbered, opaque, sealed envelopes (SNOSE) were prepared in advance, each containing the group assignment. Envelopes were opened sequentially only after participants completed baseline assessments.
Enrollment of participants and the process of obtaining informed consent were carried out by the principal investigator and research assistants. Group assignment was implemented by a separate research assistant who was not involved in generating the randomization sequence.
Outcome assessors, including registered nutritionist–dietitians (RNDs) conducting anthropometric measurements and dietary intake assessments (via three-day food diaries), and medical technologists performing biochemical analyses were blinded to allocation. By contrast, the RNDs delivering the MyGeneMyDiet® or standard recommendations could not be blinded due to the nature of the intervention.
Intervention strategy
Intervention arm (MyGeneMyDiet®)
Participants in the intervention arm received MyGeneMyDiet® recommendations, a personalized nutrition and lifestyle advice tailored to individual genotype and clinical data. Recommendations were developed by integrating genotyping results with anthropometric measures (BMI, waist circumference, body fat percentage), biochemical results, dietary intake, and physical activity data.
As previously mentioned, the MyGeneMyDiet® recommendation focused on three obesity-related polymorphisms. For carriers of the A allele of FTO rs9939609, the advice was to engage in 30–60 min of moderate-intensity aerobic physical activity daily. For carriers of the G allele of UCP1 rs1800592, the recommendation was to reduce daily caloric intake by 150 kcal below estimated requirements. For carriers of the T allele of TCF7L2 rs7903146, participants were advised to limit fat intake to 15–20% of total energy, corresponding to the lower end of standard guidelines (15–30%). Participants carrying multiple risk alleles received combined recommendations.
Intervention materials were developed through a structured decision-tree framework that integrated genotype with anthropometric, biochemical, and lifestyle data. Their content and delivery protocols underwent iterative review by a multidisciplinary Scientific Advisory Board to ensure scientific validity and cultural appropriateness. The development process, including scoping review, SNP selection, and creation of counseling tools is detailed in Supplement: Additional Method 1.
RNDs delivered the intervention through structured online counseling sessions at months 0, 3, 6, and 12. Adherence was monitored by matching recommendations to specific measures: physical activity guidance was evaluated using the International Physical Activity Questionnaire–Short Form (IPAQ-SF) for FTO carriers, caloric intake was assessed from three-day food diaries for UCP1 carriers, and percentage energy from fat was calculated from food diaries for TCF7L2 carriers. These adherence metrics were recorded during both the active (0–6 months) and follow-up (6–12 months) phases, discussed with participants during counseling, and used to reinforce or adjust guidance.
Control arm (standard recommendations)
Participants in the control arm received standard weight management advice based on national and international guidelines, including the Philippine Nutrition Practice Guidelines for the Screening and Management of Obesity, the 2012 Nutrition Guidelines for Filipinos, the Pinggang Pinoy dietary model, and the 2020 WHO Guidelines on Physical Activity. Standard recommendations included at least 150 min of moderate-intensity physical activity per week, daily energy intake based on estimated requirements (age, sex, weight, height, activity level) with a ~ 500 kcal deficit for weight loss, and macronutrient distribution of carbohydrates (55–65% of total energy), fat (25–30%), and protein (10–15%).
These recommendations were not informed by genetic data and were tailored only based on baseline anthropometry, biochemical markers, and lifestyle factors. Participants in the control arm received online counseling sessions with RNDs at months 0, 3, 6, and 12, matching the frequency of the intervention arm. After the 12-month trial, control participants were provided with their MyGeneMyDiet® recommendations as part of after-trial care.
Outcomes assessment
The pre-specified primary outcomes were differences in weight, BMI, waist circumference, and body fat percentage between arms at the end of the active phase (month 6) and the follow-up phase (month 12). Secondary outcomes included differences in blood lipid profile, glycated hemoglobin (HbA1c), and dietary and macronutrient intakes. No modifications to the pre-specified primary or secondary outcomes were made after trial commencement. Outcome assessments were scheduled during standardized morning research visits and aligned with fasting blood collection.
Anthropometric measurements
Anthropometric measurements were obtained during the standardized morning visits by trained staff using calibrated equipment. Participants wore light clothing and removed shoes and heavy items. Body weight and body fat percentage were measured using a bioelectrical impedance analyzer (Tanita MC-780, Tanita Corporation, Japan). Height was assessed with a stadiometer (Seca 217, Seca, Germany), and waist circumference was measured at the midpoint between the lowest rib and the iliac crest using a non-stretchable tape (Seca 203, Seca, Germany). All anthropometric measures were taken twice following standardized procedures, with a third measurement obtained if the difference between the two initial readings exceeded 0.3 kg for weight, 0.5 cm for height, or 0.5 cm for waist circumference. The average of the readings was recorded as the final value. BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²). Body fat percentage was classified according to the chart developed by Gallagher et al. [42].
Dietary intake
Dietary intake was assessed monthly using three-day food diaries, which included two non-consecutive weekdays and one weekend day. Participants were instructed on proper recording of food and beverage intake, including estimation of portion sizes, during an orientation session before data collection. Each diary captured details on food type, variety, brand, portion size, meal timing, preparation method, and source (e.g., home-cooked or takeaway). Food diaries were submitted in printed or electronic form prior to each follow-up session. Registered nutritionist–dietitians reviewed and verified all diaries for completeness and accuracy approximately two weeks before each counseling session. Nutrient analysis was conducted using the Philippine Food Composition Tables (PhilFCT), supplemented by international food composition databases when needed. Dietary intake at each timepoint was summarized as the mean of the three recorded days to account for differences in eating patterns across the week and provide a more reliable estimate of usual dietary intake.
Biochemical data
Fasting venous blood samples (approximately 5 mL) were collected at baseline and at months 3, 6, 9, and 12. Participants were instructed to fast for 10–12 h before each collection. Samples were analyzed for HbA1c and lipid profiles by a third-party certified laboratory. HbA1c was measured using ion-exchange high-performance liquid chromatography (HPLC), and lipid profiles were determined using enzymatic colorimetric methods. Daily quality control procedures were conducted with both low- and high-control samples, and all analyses were required to meet predefined standards before participant samples were processed.
Adverse event monitoring
All reported adverse events were reviewed by the principal investigator and the study physician to determine severity and potential relation to the intervention or study procedures. Reportable events were promptly communicated to the institutional ethics review committee, in line with approved protocols. Participant safety was monitored throughout the 12-month trial period.
Statistical analysis
Analyses were conducted according to randomized group assignment. Because outcome data were incomplete at follow-up due to attrition and pre-intervention non-initiation, primary analyses used available-case data at each timepoint. To evaluate the robustness of findings to missing outcomes data, we performed sensitivity analyses addressing missingness using last observation carried forward (LOCF) and inverse probability of attrition weighting (IPAW). IPAW weights were derived from baseline characteristics, including age, sex, job classification, and weight. Participants were analyzed in their originally assigned groups.
Continuous variables are presented as mean ± standard deviation (SD), and categorical variables as counts and percentages. Normality of data distribution was assessed using the Shapiro–Wilk test, and homogeneity of variances between groups was evaluated with Levene’s test. Between-group differences were examined using analysis of covariance (ANCOVA), with baseline measurements entered as covariates. Within-group differences over time were assessed using paired t-tests; these were performed to describe temporal changes and participant responses but not to test comparative effectiveness, which was evaluated through between-group analyses.
All statistical analyses were performed using SPSS version 26 and R version 4.2.3. Statistical significance was set at P < 0.05.
Results
Study participants
A total of 136 individuals were screened for eligibility. Following genetic testing, those who did not carry any of the target risk variants were excluded prior to randomization. Sixty-six participants were randomized in equal numbers to the MyGeneMyDiet® (n = 33) and control (standard recommendation, n = 33) groups. Of these, 52 attended the baseline visit and initiated the intervention (MyGeneMyDiet®, n = 29; standard recommendation, n = 23), while 14 did not initiate baseline measurements (withdrawn n = 1; lost to follow-up n = 13). At month 6, outcome data were available for 49 participants (MyGeneMyDiet®, n = 26; standard recommendations, n = 23), and 27 participants completed follow-up at month 12 (MyGeneMyDiet®, n = 15; standard recommendations, n = 12). Participant flow is summarized in Fig. 1.
Fig. 1.
CONSORT flow diagram of participants in the MyGeneMyDiet® randomized controlled trial. Participant numbers are shown at each stage (screening, randomization, allocation, follow-up, and analysis), with reasons for non-initiation and attrition. Primary endpoints were assessed at months 6 and 12; intermediate visits (months 3 and 9) are not shown. Reasons for exclusions and attrition are indicated
Baseline characteristics of participants who initiated the intervention (n = 52) are presented in Table 1. Overall, the sample was predominantly female and most participants were aged 19–39 years. Anthropometric measures, biochemical markers, and dietary intake are summarized descriptively by group.
Table 1.
Participant characteristics at randomization
| All participants (n = 52) | Standard Recommendation (n = 23) |
MyGeneMyDiet® (n = 29) |
|
|---|---|---|---|
| Sex | |||
| Male | 22 (42.3) | 13 (56.5) | 9 (31.0) |
| Female | 30 (57.7) | 10 (43.5) | 20 (69.0) |
| Age, mean (SD), years | 33.6 (10.2) | 34.5 (11.6) | 32.8 (9.1) |
| Age, n (%) | |||
| 19–29 | 23 (44.2) | 9 (39.1) | 14 (48.3) |
| 30–39 | 18 (34.6) | 8 (34.8) | 10 (34.5) |
| 40–49 | 5 (9.6) | 2 (8.7) | 3 (10.3) |
| 50–59 | 6 (11.5) | 4 (17.4) | 2 (6.9) |
| Occupation | |||
| Managers | 19 (36.5) | 8 (34.8) | 11 (37.9) |
|
Technicians/associate professionals |
19 (36.5) | 8 (34.8) | 11 (37.9) |
| Clerical support workers | 14 (26.9) | 4 (17.4) | 10 (34.5) |
| Student | 3 (5.8) | 3 (13.0) | 0 (0.0) |
| Unemployed | 2 (3.8) | 1 (4.3) | 1 (3.4) |
| Anthropometry | |||
| Weight, mean (SD), kg | 76.7 (14.1) | 76.6 (14.2) | 76.8 (14.2) |
| BMI, mean (SD), kg/m2 | 29.5 (4.0) | 29.0 (2.9) | 29.9 (4.6) |
| Waist circumference, cm | 90.7 (10.1) | 90.2 (9.2) | 91.0 (10.9) |
| Body fat (%) | 36.3 (7.9) | 33.8 (7.6) | 38.3 (7.7) |
| Biochemical markers | |||
| HbA1c (%) | 5.5 (0.4) | 5.6 (0.4) | 5.5 (0.4) |
| Total cholesterol (mmol/L) | 5.4 (1.0) | 5.5 (1.0) | 5.4 (1.0) |
| HDL-cholesterol (mmol/L) | 1.3 (0.3) | 1.3 (0.3) | 1.4 (0.3) |
| LDL-cholesterol (mmol/L) | 3.4 (0.9) | 3.5 (0.9) | 3.4 (0.9) |
| Triglycerides (mmol/L) | 1.5 (0.7) | 1.6 (0.9) | 1.4 (0.6) |
| Dietary intake | |||
| Total calories (kcal) | 1928.7 (523.4) | 2036.2 (557.0) | 1843.5 (488.0) |
| Protein (g) | 75.4 (24.8) | 79.5 (23.1) | 72.1 (25.9) |
| Carbohydrates (g) | 215.2 (73.4) | 226.6 (89.0) | 206.2 (58.3) |
| Fat (g) | 81.5 (33.3) | 86.1 (35.5) | 77.9 (31.5) |
Values are reported as n (%) or mean (SD). Occupations are based on the 2012 Philippine Standard Occupational Classification
In terms of genotype distribution, the AA variant of FTO rs9939609 was rare, observed only in the MyGeneMyDiet® group (n = 3, 10.3%) (Supplement: Additional Table 2). The TT genotype of TCF7L2 rs7903146 was predominant in both groups (MyGeneMyDiet®: n = 29, 100%; standard recommendation: n = 21, 91.3%). For UCP1 rs1800592, the GA genotype was most common, found in 18 of 29 participants (62.1%) in the MyGeneMyDiet® group and 12 of 23 participants (52.2%) in the standard recommendation group.
Engagement with the intervention
Adherence to the MyGeneMyDiet® and standard weight-loss recommendations were evaluated at months 3, 6, 9, and 12 using food diaries and IPAQ-SF questionnaires collected before counseling sessions. Adherence was defined against three behavioral targets corresponding to the genotype-informed recommendations: total caloric intake (UCP1), fat intake (TCF7L2), and physical activity (FTO). A summary of adherence rates is presented in Supplement: Additional Table 3.
Overall adherence was low in both groups, with fewer than half of the participants consistently meeting the behavioral targets at any time point. No clear differences in adherence were observed between the intervention and standard recommendation arms.
Weight reduction across timepoints
Both groups experienced weight reduction over the 12-month trial (Fig. 2). In the MyGeneMyDiet® arm, mean body weight decreased from 76.7 kg at baseline to 71.7 kg at month 12, corresponding to an average loss of 6.5%. The standard recommendation arm showed a similar trend, with weight decreasing from 76.6 to 71.7 kg, an average loss of 6.4%. No significant differences in weight change were observed between groups at any time point.
Fig. 2.
Changes in body weight over 12 months in the MyGeneMyDiet® and standard recommendation groups. Mean body weight (kg) at baseline and months 3, 6, 9, and 12 are shown for participants in the MyGeneMyDiet® and standard recommendation groups. Error bars represent standard deviations
Comparison of outcomes between intervention groups at month 6 (active phase)
At month 6, adjusted between-group comparisons showed no statistically significant differences for anthropometric outcomes, lipid profile measures, or dietary intake (Table 2A). A small difference was observed in HbA1c (adjusted mean difference: 0.11, 95% CI: 0.01, 0.20; P = 0.038).
Table 2A.
Between-group comparisons at month 6 (Active Phase)
| Outcome | Month 6 (Active Phase) | Difference [95% CI] |
P-value* | |
|---|---|---|---|---|
| Standard Recommendation (n = 23) |
MyGeneMyDiet® (n = 26) |
|||
| Anthropometry | ||||
| Weight (kg) | 75.2 | 75.5 | -0.36 [-1.77, 1.04] | 0.604 |
| BMI (kg/m2) | 29.2 | 29.1 | 0.11 [-0.51, 0.73] | 0.716 |
| Waist circumference (cm) | 88.6 | 88.9 | -0.27 [-2.23, 1.69] | 0.781 |
| Body fat (%) | 36.1 | 36.0 | 0.92 [-0.86, 1.05] | 0.848 |
| Biochemical markers | ||||
| HbA1c (%) | 5.6 | 5.5 | 0.11 [0.01, 0.20] | 0.038 |
| Total cholesterol (mmol/L) | 5.2 | 5.3 | -0.12 [-0.57, 0.32] | 0.222 |
| HDL-cholesterol (mmol/L) | 1.3 | 1.3 | 0.07 [-0.05, 0.18] | 0.241 |
| LDL-cholesterol (mmol/L) | 3.2 | 3.2 | -0.05 [-0.48, 0.39] | 0.826 |
| Triglycerides (mmol/L) | 1.5 | 1.8 | -0.28 [-0.99, 0.42] | 0.420 |
| Dietary intake | ||||
| Total calories (kcal) | 1667.5 | 1524.4 | 143.1 [-138.50, 424.71] | 0.312 |
| Protein (g) | 65.7 | 62.4 | 3.34 [-7.80, 14.48] | 0.549 |
| Carbohydrates (g) | 194.9 | 171.3 | 23.62 [-9.98, 57.23] | 0.164 |
| Fat (g) | 67.5 | 66.6 | 0.94 [-14.96, 16.85] | 0.905 |
Abbreviations: HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein
Values are adjusted means with 95% confidence intervals, derived from analysis of covariance (ANCOVA) controlling for baseline measurements. *P values represent between-group comparisons at month 6. Analyses used all available participants with non-missing values for the outcome at both baseline and month 6 (standard recommendation, n = 23; MyGeneMyDiet®, n = 26)
Comparison of outcomes between intervention groups at month 12 (inactive phase)
At month 12, adjusted between-group comparisons showed no statistically significant differences for anthropometric or biochemical outcomes (Table 2B). Adjusted between-group differences in dietary intake did not reach statistical significance, and confidence intervals were wide.
Table 2B.
Between-group comparisons at month 12 (Inactive Phase)
| Outcome | Month 12(Inactive Phase) | Difference [95% CI] |
P-value* | |
|---|---|---|---|---|
| Standard Recommendation (n = 12) |
MyGeneMyDiet® (n = 15) |
|||
| Anthropometry | ||||
| Weight (kg) | 71.7 | 71.7 | 0.04 [-3.41, 3.49] | 0.981 |
| BMI (kg/m2) | 28.0 | 27.9 | 0.15 [-1.24, 1.52] | 0.831 |
| Waist circumference (cm) | 84.5 | 84.6 | -0.12 [-4.58, 4.33] | 0.955 |
| Body fat (%) | 34.9 | 34.6 | 0.27 [-2.02, 2.56] | 0.809 |
| Biochemical markers | ||||
| HbA1c (%) | 5.5 | 5.6 | -0.10 [-0.32, 0.10] | 0.306 |
| Total cholesterol (mmol/L) | 5.4 | 5.2 | 0.22 [-0.49, 0.93] | 0.522 |
| HDL-cholesterol (mmol/L) | 1.4 | 1.4 | 0.04 [-0.09, 0.19] | 0.480 |
| LDL-cholesterol (mmol/L) | 3.4 | 3.3 | 0.06 [-0.55, 0.68] | 0.828 |
| Triglycerides (mmol/L) | 1.5 | 1.2 | 0.28 [-0.26, 0.82] | 0.290 |
| Dietary intake | ||||
| Total calories (kcal) | 1677.7 | 1324.4 | 353.3 [-84.00, 790.58] | 0.108 |
| Protein (g) | 75.1 | 54.9 | 20.21 [-3.23, 43.65] | 0.088 |
| Carbohydrates (g) | 193.3 | 164.9 | 28.47 [-12.38, 69.31] | 0.163 |
| Fat (g) | 65.4 | 51.1 | 14.33 [-12.24, 40.90] | 0.277 |
Abbreviations: HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; LDL, low-density lipoprotein
Values are adjusted means with 95% confidence intervals, derived from analysis of covariance (ANCOVA) controlling baseline measurements. *P values represent between-group comparisons at each time point, obtained using ANCOVA
Within-group changes from baseline
Paired baseline – month 6 outcome data were available for 23/23 participants in the standard recommendation arm and 26/29 in the MyGeneMyDiet® arm. In the standard recommendation group, total energy decreased by 359.1 kcal (P = 0.030) and protein intake by 12.2 g (P = 0.042). In the MyGeneMyDiet® group, total energy intake decreased by 295.8 kcal (P = 0.015) and carbohydrate intake by 35.5 g (P = 0.010). Within-group changes in weight, BMI, body fat percentage, and biochemical markers were not statistically significant in either group (Table 3).
Table 3.
Changes in outcome variables in month 6 relative to baseline (within-group)
| Standard Recommendation (n = 23) |
MyGeneMyDiet® (n = 26) |
|||||||
|---|---|---|---|---|---|---|---|---|
| Baseline | Month 6 | Change | P-value* | Baseline | Month 6 | Change | P-value* | |
| Anthropometry | ||||||||
| Weight (kg) | 76.6 ± 14.2 | 75.9 ± 13.4 | -0.7 | 0.179 | 75.3 ± 13.5 | 74.9 ± 14.6 | -0.4 | 0.447 |
| BMI (kg/m2) | 29.0 ± 2.9 | 29.0 ± 3.0 | 0.0 | 0.742 | 29.5 ± 4.1 | 29.4 ± 4.4 | -0.5 | 0.352 |
| Waist circumference (cm) | 90.2 ± 9.2 | 88.7 ± 8.8 | -1.5 | 0.009 | 90.0 ± 10.5 | 88.8 ± 11.3 | -1.2 | 0.131 |
| Body fat (%) | 33.8 ± 7.6 | 33.6 ± 8.2 | -0.2 | 0.551 | 38.3 ± 6.8 | 38.1 ± 6.7 | -0.2 | 0.486 |
| Blood parameters | ||||||||
| HbA1c (%) | 5.6 ± 0.4 | 5.6 ± 0.3 | 0.0 | 0.103 | 5.5 ± 0.4 | 5.5 ± 0.4 | 0.0 | 0.655 |
| Total cholesterol (mmol/L) | 5.5 ± 1.0 | 5.2 ± 1.0 | -0.3 | 0.246 | 5.4 ± 1.0 | 5.3 ± 1.0 | -0.1 | 0.294 |
| HDL-cholesterol (mmol/L) | 1.3 ± 0.3 | 1.3 ± 0.3 | 0.0 | 0.483 | 1.4 ± 0.3 | 1.3 ± 0.3 | -0.1 | 0.072 |
| LDL-cholesterol (mmol/L) | 3.5 ± 0.9 | 3.2 ± 0.9 | -0.3 | 0.180 | 3.4 ± 0.9 | 3.2 ± 1.0 | -0.2 | 0.087 |
| Triglycerides (mmol/L) | 1.6 ± 0.9 | 1.5 ± 0.6 | -0.1 | 0.773 | 1.4 ± 0.6 | 1.7 ± 1.7 | 0.3 | 0.341 |
| Dietary intake | ||||||||
| Total calories (kcal) | 2036.2 ± 556.9 | 1677.1 ± 437.5 | -359.1 | 0.030 | 1811.7 ± 443.0 | 1515.9 ± 502.2 | -295.8 | 0.015 |
| Protein (g) | 79.5 ± 23.1 | 67.3 ± 18.4 | -12.2 | 0.042 | 68.7 ± 21.1 | 61.0 ± 20.5 | -7.7 | 0.085 |
| Carbohydrates (g) | 226.6 ± 89.0 | 196.1 ± 60.8 | -30.5 | 0.184 | 205.7 ± 54.8 | 170.2 ± 54.8 | -35.5 | 0.010 |
| Fat (g) | 86.1 ± 35.5 | 68.7 ± 27.9 | -17.4 | 0.058 | 75.7 ± 29.6 | 65.5 ± 27.7 | -10.2 | 0.126 |
Abbreviations: HbA1c – glycated hemoglobin aka hemoglobin A1c; HDL- high-density lipoprotein; LDL – low-density lipoprotein
Values are presented as mean ± SD among participants with non-missing paired measurements at both baseline and Month 6 (complete-case paired observations); *P values were obtained using paired t-test comparing month 6 vs. baseline within each group. Baseline values shown in this table therefore reflect the subset included in the paired month 6 analysis. Negative change values indicate a reduction from baseline
At month 12 (Table 4), reductions in dietary intake persisted. In the MyGeneMyDiet® group, decreases were observed for total energy and macronutrients (energy: -461.4 kcal, P = 0.001; protein: -12.1 g, P = 0.007; carbohydrate: -46.4 g, P = 0.015; fat = -22.0 g, P = 0.014). In the standard recommendation group, within-group dietary changes at month 12 did not reach statistical significance. Waist circumference decreased in the standard recommendation group over 12 months (-3.2 cm, P = 0.010); other anthropometric and biochemical changes were not statistically significant within groups.
Table 4.
Changes in outcome variables in month 12 relative to baseline (within-group)
| Standard Recommendation (n = 12) |
MyGeneMyDiet® (n = 15) |
||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Baseline | Month 12 | Change | P-value* | Baseline | Month 12 | Change | P-value* | ||||
| Anthropometry | |||||||||||
| Weight (kg) | 72.9 ± 11.9 | 71.7 ± 11.4 | -1.2 | 0.227 | 72.9 ± 10.7 | 71.7 ± 13.3 | -1.2 | 0.343 | |||
| BMI (kg/m2) | 28.3 ± 1.5 | 27.9 ± 1.9 | -0.4 | 0.225 | 28.6 ± 3.0 | 28.0 ± 3.7 | -0.6 | 0.281 | |||
| Waist circumference (cm) | 87.8 ± 4.7 | 84.6 ± 4.6 | -3.2 | 0.010 | 87.4 ± 7.5 | 84.4 ± 9.3 | -3.0 | 0.117 | |||
| Body fat (%) | 33.7 ± 7.0 | 32.7 ± 7.2 | -1.0 | 0.055 | 37.6 ± 6.9 | 36.3 ± 7.3 | -1.0 | 0.154 | |||
| Blood parameters | |||||||||||
| HbA1c (%) | 5.6 ± 0.4 | 5.5 ± 0.4 | -0.1 | 0.412 | 5.5 ± 0.5 | 5.5 ± 0.6 | 0.0 | 0.546 | |||
| Total cholesterol (mmol/L) | 5.8 ± 1.0 | 5.5 ± 1.0 | -0.3 | 0.216 | 5.4 ± 1.0 | 5.1 ± 0.9 | -0.3 | 0.318 | |||
| HDL-cholesterol (mmol/L) | 1.3 ± 0.3 | 1.4 ± 0.3 | 0.1 | 0.487 | 1.4 ± 0.4 | 1.4 ± 0.3 | 0.0 | 0.487 | |||
| LDL-cholesterol (mmol/L) | 3.8 ± 0.9 | 3.5 ± 1.0 | -0.3 | 0.144 | 3.4 ± 0.9 | 3.2 ± 0.8 | -0.2 | 0.387 | |||
| Triglycerides (mmol/L) | 1.5 ± 0.5 | 1.5 ± 0.9 | 0.0 | 0.891 | 1.3 ± 0.5 | 1.2 ± 0.4 | -0.1 | 0.350 | |||
| Dietary intake | |||||||||||
| Total calories (kcal) | 2025.3 ± 716.8 | 1718.3 ± 620.2 | -307 | 0.236 | 1753.3 ± 359.3 | 1291.9 ± 466.5 | -461.4 | 0.001 | |||
| Protein (g) | 79.1 ± 26.9 | 79.8 ± 40.6 | 0.7 | 0.955 | 63.3 ± 18.5 | 51.2 ± 16.3 | -12.1 | 0.007 | |||
| Carbohydrates (g) | 239.8 ± 109.0 | 199.7 ± 46.4 | -40.1 | 0.128 | 206.2 ± 57.7 | 159.8 ± 63.7 | -46.4 | 0.015 | |||
| Fat (g) | 83.3 ± 39.0 | 67.0 ± 43.1 | -16.3 | 0.288 | 71.8 ± 27.5 | 49.8 ± 22.3 | -22.0 | 0.014 | |||
Abbreviations: HbA1c – glycated hemoglobin aka hemoglobin A1c; HDL- high-density lipoprotein; LDL – low-density lipoprotein. Values are presented as mean ± SD; *P values were obtained using paired t-tests. Baseline values reflect participants with paired baseline and month 12 measurements included in this table. The minus sign (-) indicates a reduction from baseline. All other change values indicate no change or an increase
Adverse events
As described in the Methods, all adverse events were reviewed by the principal investigator and study physician. During the 12-month trial period, no serious adverse events attributable to the interventions were observed.
Additional analyses to address attrition and adherence
To account for potential bias from attrition and adherence, sensitivity analyses were performed using Inverse Probability of Attrition Weighting (IPAW) and Last Observation Carried Forward (LOCF). These methods were applied to address missing data and evaluate the robustness of trial findings. Results are presented separately for the active phase (Supplement: Additional Table 4) and the inactive phase (Supplement: Additional Table 5). Findings from both IPAW and LOCF analyses were largely consistent with those from the raw, unadjusted, complete case analysis.
Discussion
This study evaluated the utility of gene-based versus standard weight recommendations on anthropometric, specific biochemical markers (glucose and lipid profile), and dietary outcomes over 12 months. Although within-group changes were observed, we found no evidence of meaningful between-group differences between the MyGeneMyDiet® and standard recommendation groups across most outcomes at both the active (month 6) and inactive (month 12) phases.
No meaningful differences in anthropometric outcomes were observed between the groups throughout the trial. While some studies have demonstrated reductions in BMI, body weight, and body fat with diets tailored to specific genetic polymorphisms [43–45], our findings are consistent with studies that reported no added benefit from nutrigenetic-guided diets [35, 41]. This lack of advantage may reflect the multifactorial nature of weight regulation, where genetic predisposition is only one component of a complex system influenced by baseline metabolic health, psychosocial readiness for dietary changes, and adherence to interventions. Recent trials and reviews further support our findings. A large randomized controlled trial evaluated a polygenic, genetically informed weight-loss strategy and found no added benefit over conventional dietary advice [46]. Even with a longer intervention period and broader genetic profiling, the intervention did not result in superior outcomes in weight or metabolic markers. Similarly, a systematic review of gene-diet interaction studies reported inconsistent effects across trials, with most interactions lacking statistical significance and replication. Notably, FTO rs9939609 – one of the most studied variants – did not show a consistent relationship with dietary intake or BMI [47].
Previous studies examining macronutrient specific gene-diet interactions have reported improvements in HDL-cholesterol, triglycerides, and inflammatory markers [45, 48, 49]. However, apart from the reduction in HbA1c level in the MyGeneMyDiet® group at month 6, we did not observe significant improvements in biochemical markers. This may be due to low adherence or the broadly targeted nature of our recommendations, which, while genotype-informed, were limited to three SNPs and addressed general behaviors such as caloric intake, fat intake, and physical activity.
Dietary intake decreased over time in both groups, and within-group reductions were observed in the MyGeneMyDiet® arm at certain timepoints. These patterns align with evidence suggesting that personalized recommendations, especially those framed as genetically-informed, may initially enhance engagement and perceived relevance [25, 26, 35, 50]. However, these reported dietary changes did not translate into clear between-group differences in anthropometry or biochemical markers. Sex-related differences in energy requirements and dietary reporting may influence dietary patterns [51–53].
Despite reported reductions in energy and macronutrient intake, adherence to genotype-informed behavioral targets, including total energy, fat intake, and physical activity, remained low throughout the trial. This has likely contributed to the limited between-group differences observed, as sustained adherence is essential for gene-diet interactions to yield measurable physiological benefits. The use of self-reported food diaries, while practical during the pandemic, may have introduced measurement error. Absolute energy and macronutrient intakes should be interpreted cautiously because intake varies by body size and sex and self-reported dietary data are prone to under-reporting. Accordingly, we emphasized within-person changes over time and baseline-adjusted between-group comparisons rather than interpreting absolute intakes in isolation, and we did not standardize intake relative to body size. Physical activity was assessed using the IPAQ-SF, which relies on self-report and may be subject to recall and social desirability bias, potentially leading to misclassification of activity levels. The modest nature of behavioral changes, combined with potential inaccuracies in reporting, may partly explain why improvements in dietary behaviors did not translate into significant changes in weight or other physiological outcomes.
The apparent reduction in body weight by month 12, despite relatively modest changes by month 6, should be interpreted cautiously. The trial did not include objective measures that could confirm changes in physical activity or other behaviors during the inactive phase, and adherence indicators were based on self-report. Differential retention may also have influenced observed longer-term patterns if participants who remained in follow-up were more motivated or better positioned to sustain lifestyle changes. These considerations limit causal interpretation of why changes appeared more pronounced later in follow-up.
Several limitations warrant emphasis. Attrition was substantial, and outcome data were incomplete at follow-up, particularly at month 12, reducing precision and limiting interpretability. Primary analyses therefore relied on available-case data at each timepoint; consequently, results reflect analyses by randomized assignment with incomplete follow-up rather than a fully observed intention-to-treat dataset. Although analyses were evaluated using sensitivity approaches (LOCF and inverse probability of attrition weighting), attrition bias remains possible if loss to follow-up was related to participant characteristics or outcomes. The gene-based intervention itself was derived from three SNPs; common variants typically have modest individual effects on complex traits such as weight regulation, and the limited genetic scope may constrain the ability of genotype-informed advice to outperform standard counseling. Finally, sex distribution differed between groups. While sex was included among the baseline predictors used to derive IPAW weights, the available follow-up sample size did not permit adequately powered stratified analyses, and residual confounding by sex cannot be excluded.
The trial overlapped with COVID-19 pandemic, which may have influenced follow-up logistics and measurement quality through disruptions to routine schedules, mobility, and attendance at follow-up visits. Remote delivery and reliance on self-reported measures were practical solutions but may have contributed to reduced engagement and increased measurement error in dietary and physical activity assessments. These contextual factors are relevant when interpreting adherence patterns, attrition, and the precision of behavioral measures.
The two-phase design of the trial provided unique insights into the immediate and long-term impacts of personalized interventions. Offering genetic counseling may have further enhanced participant engagement by providing personalized context for the recommendations. This points to the value of integrating education and support into personalized nutrition strategies.
To our knowledge, this is among the first studies to evaluate the gene-based weight-loss interventions in low- and middle-income countries (LMIC) setting. Conducting nutrigenomics research in resource-limited environments poses challenges, as highlighted in our previous work [54]. These included limited genomic literacy among participants, infrastructural barriers, and sociocultural factors influencing dietary and lifestyle behaviors. Additionally, broader systemic challenges such as access to healthcare and nutrition education further complicate implementation. Tailoring interventions to these unique contexts is essential for the scalability and sustainability of personalized nutrition strategies in LMICs.
Taken together, our findings suggest that gene-based and standard recommendations may yield comparable outcomes in real-world, resource-constrained environments. While personalization may support modest reported dietary changes, it did not confer clear advantages over standard approaches for improving anthropometric or biochemical outcomes in this trial.
Conclusion and future perspectives
In this trial, we found no evidence of meaningful differences between gene-based and standard nutritional recommendations in anthropometric, biochemical, or dietary outcomes over 12 months. Future research should focus on understanding the factors that influence adherence, optimizing the delivery of personalized recommendations, and evaluating their effectiveness in diverse real-world settings.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
We thank the administrative and infrastructure support of DOST-Food and Nutrition Research Institute, and the study participants for their commitment to the trial. We would also like to thank the members of the MyGeneMyDiet® Scientific Advisory Board, namely, Dr. Celeste C. Tanchoco, Dr. Liezl M. Atienza, Dr. Ma-am Joy R. Tumulak, Ms. Zenaida F. Velasco, Mr. Peter James B. Abad, Dr. Jan Paolo D. Dipasupil, and Ms. Virgith B. Buena. Likewise, the authors would like to acknowledge Rachel Evangelista, Krishna Borjal, and Joven Hayagan for operational support.
Abbreviations
- BMI
Body mass index
- DNA
Deoxyribonucleic acid
- FCT
Food Composition Table
- FIERC
FNRI Institutional Ethics Review Committee
- FNRI
Food and Nutrition Research Institute
- FTO
Fat mass and obesity-associated gene
- HbA1c
Glycated hemoglobin
- HPLC
High-performance liquid chromatography
- IPAQ-SF
International Physical Activity Questionnaire – Short Form
- IPAW
Inverse Probability of Attrition Weighting
- LOCF
Last Observation Carried Forward
- RNDs
Registered Nutritionist-Dietitians
- SD
Standard deviation
- SNOSE
Sequentially numbered, opaque sealed envelope; s
- TCF7L2
Transcription factor 7-like 2 gene
- UCP1
Uncoupling protein 1 gene
- WHO
World Health Organization
Author contributions
JSN, JPHL: conceived and designed the study; JSN, JPHL, DGDR, MPR: development of overall research plan; JSN, JCR, GBG: analyzed the data; JSN, JPHL, DGDR, MPR, MLM, RDF, NLCS: study oversight; AMFDD, JJVC, MGF, DJVF, DASM, MVP, HSSM, RVMC, AAB, GMA, RCB: conducted research, collected data, assembled and organized the data; FJBD, GBG: guidance and oversight during the data analysis and manuscript preparation; JSN: wrote the paper and had primary responsibility for the final content. All authors critically reviewed the manuscript and approved of the final version.
Funding
This research was funded by the Department of Science and Technology – Food and Nutrition Research Institute through the Locally Funded Project (LFP) of the Philippines’ Department of Budget and Management.
Data availability
Data for this study is available upon request through a data transfer agreement with the Food and Nutrition Research Institute. Interested researchers may contact the corresponding author to initiate the request.
Declarations
Ethics approval and consent to participate
The study adhered to the Declaration of Helsinki and received ethical approval from the Food and Nutrition Research Institute (FNRI) Institutional Ethics Review Committee (FIERC-2021-001), Taguig City, Philippines. Written informed consent was obtained from all participants.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Jacus Nacis, Email: jacus1.nacis@wur.nl, Email: jacusnacis@gmail.com.
Gerard Bryan Gonzales, Email: Bryan.Gonzales@UGent.be.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Data for this study is available upon request through a data transfer agreement with the Food and Nutrition Research Institute. Interested researchers may contact the corresponding author to initiate the request.