A Proactive Intervention Study in Metabolic Syndrome High-Risk Populations Using Phenome-Based Actionable P4 Medicine Strategy

Abstract

The integration of predictive, preventive, personalized, and participatory (P4) healthcare advocates proactive intervention, including dietary supplements and lifestyle interventions for chronic disease. Personal profiles include deep phenotypic data and genetic information, which are associated with chronic diseases, can guide proactive intervention. However, little is known about how to design an appropriate intervention mode to precisely intervene with personalized phenome-based data. Here, we report the results of a 3-month study on 350 individuals with metabolic syndrome high-risk that we named the Pioneer 350 Wellness project (P350). We examined: (1) longitudinal (two times) phenotypes covering blood lipids, blood glucose, homocysteine (HCY), and vitamin D₃ (VD₃), and (2) polymorphism of genes related to folic acid metabolism. Based on personalized data and questionnaires including demographics, diet and exercise habits information, coaches identified 'actionable possibilities', which combined exercise, diet, and dietary supplements. After a 3-month proactive intervention, two-thirds of the phenotypic markers were significantly improved in the P350 cohort. Specifically, we found that dietary supplements and lifestyle interventions have different effects on phenotypic improvement. For example, dietary supplements can result in a rapid recovery of abnormal HCY and VD₃ levels, while lifestyle interventions are more suitable for those with high body mass index (BMI), but almost do not help the recovery of HCY. Furthermore, although people who implemented only one of the exercise or diet interventions also benefited, the effect was not as good as the combined exercise and diet interventions. In a subgroup of 226 people, we examined the association between the polymorphism of genes related to folic acid metabolism and the benefits of folate supplementation to restore a normal HCY level. We found people with folic acid metabolism deficiency genes are more likely to benefit from folate supplementation to restore a normal HCY level. Overall, these results suggest: (1) phenome-based data can guide the formulation of more precise and comprehensive interventions, and (2) genetic polymorphism impacts clinical responses to interventions. Notably, we provide a proactive intervention example that is operable in daily life, allowing people with different phenome-based data to design the appropriate intervention protocol including dietary supplements and lifestyle interventions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00115-z.

Introduction

Metabolic syndromes are a cluster of conditions including hyperlipidemia, hypertension, hyperglycemia, obesity, etc. (Sangouni et al. 2022). Each individual has a unique and complex set of lifestyle, nutrition, genetic, and environmental factors that impact phenotypes and contribute to the manifestation of metabolic syndromes (Sasso et al. 2022). Without early intervention, metabolic syndrome will develop into more serious chronic diseases such as cardiovascular diseases and type 2 diabetes (Dong et al. 2022; Samakar et al. 2022). The P4 health spectrum is a framework that aims to promote and improve healthspan by utilizing a continuum consisting of four dimensions: Predictive, Preventive, Personalized, and Participatory (Auffray et al. 2010). These dimensions refer to a range of measures taken to promote health, including the use of predictive models to forecast disease risk, preventive measures to avoid disease occurrence, the provision of personalized healthcare services to meet individual needs, and encouragement of individual participation in health management and decision-making (Sagner et al. 2017). The P4 Health Spectrum is a comprehensive model designed to facilitate innovation in the healthcare sector and improve people's overall health status (Sagner et al. 2017). In this study, we applied the concept of P4 health spectrum throughout the entire experimental process. We predicted high-risk metabolic syndrome populations using phenotype biomarkers, implemented intervention measures to prevent disease occurrence, provided personalized and precise intervention recommendations, and ultimately encouraged individual participation in deciding the intervention approach. Through the P4 health spectrum, a proactive and evidence-based intervention in the pre-clinical phase can effectively reverse the progression of the disease (Price et al. 2017; Sagner et al. 2017). For this reason, we use personalized and longitudinal data to systematically study the phenotypic and genetic data of each individual, discover the underlying laws of associations, then find intervention methods that fit one's key health metrics.

While there is strong scientific interest in using multi-dimensional data to study interventions in people with high-risk metabolic syndromes, to date little value has been demonstrated for consumers or patients. For example, some interventions are difficult to practice in daily life, such as taking probiotic formulas that are not readily available on the market (Canfora et al. 2017; Yang et al. 2015). In addition, almost all evidence-based medicine studies use a single intervention, instead of experiments with a single patient (N = 1), which not only ignores cohort heterogeneity but also makes it impossible to improve and compare different intervention methods (Ciubotaru et al. 2015; Wilson et al. 2018). These phenomena all lead to the fact that these studies cannot directly guide real-world practice.

Because of the important effects of lifestyle and dietary supplements on chronic disease risk, studies have examined the effectiveness of health coaching in improving clinical phenotypes (Ciubotaru et al. 2015; Khera et al. 2016; LeBlanc et al. 2018; Liu et al. 2020; Skulas-Ray et al. 2019). In addition, the relationship between dietary supplements and genetic polymorphisms has also been explored. It should be noted that homocysteine (HCY) has become a health indicator for cardiovascular and cerebrovascular diseases and metabolic diseases, high HCY has been called the “fourth-highest” after hyperglycemia. Therefore, HCY is an important criterion with the adult HCY level higher than 10 μmol/L being hyperhomocysteinemia as suggested by experts (Fan et al. 2017; Herrmann and Herrmann 2022; Hoogeveen et al. 2000; Pusceddu et al. 2019, 2020; Vollset et al. 2001; Woo et al. 2014). Two main reasons are responsible for the lack of folic acid: (1) insufficient intake of folic acid; and (2) low utilization of folic acid due to genetic defects. Hyperhomocysteinemia caused by folic acid deficiency caused by mutation of folic acid metabolism-related genes can be better controlled by folic acid supplementation. But this conclusion has not been validated in the intervention study (Liu et al. 2021). For some chronic diseases, personalized and precise treatments based on the P4 health concept have been shown to be highly effective (Price et al. 2017). Some studies demonstrate that a comprehensive and individualized approach can improve wellness and counteract functional decline or even reverse disease progression (Chen et al. 2012; Schechter et al. 2020). But the studies use a series of highly complex and professional intervention programs that are almost impossible to practice in daily life and achieve the desired effect, which supports the need for further research.

To address these gaps, a personalized intervention model that is easy to operate in daily life is urgently needed. This study aimed to propose a model to spur the paradigm shift to P4 medicine, with a focus on health management of P350, which reveals the association between genotype and phenotype, leading to more systematic personalized intervention methods, and will help maintain wellness and prolong the healthspan. The concept of P4 health was first proposed by Leroy Hood's group in 2010 (Auffray et al. 2010). Previous study reported on the Pioneer 100 Wellness Project (P100), which focused on the European population (Price et al. 2017). This project served as the pilot for the 100,000 (100 K) person wellness project proposed in 2014 (Hood and Price 2014). The P4 health concept has gradually gained wide popularity (Chen et al. 2012, 2022; Ho et al. 2014; Manor et al. 2018; Schussler-Fiorenza Rose et al. 2019; Wang and Wang 2012). However, there has not yet been a large-scale clinical intervention project based on P4 in the Chinese population. We developed a P4 medicine-based approach, which combines phenotypic data and genetics with lifestyle coaching. For each individual, we generated personal, dense, dynamic data (PD3) clouds, which can aid in identifying unique actionable possibilities to optimize wellness and reverse disease (Sagner et al. 2017). A critical component of the P4 health spectrum is regular interaction with a registered nutritionist or other allied healthcare providers for education and behavior modification (Sagner et al. 2017).

Here, we report on the results of an observational study of P4 patterns in 350 people with high-risk metabolic syndrome (Preventive). We quantified longitudinal clinical and metabolic markers changes in three months for each participant (Personalized) (Anjo et al. 2013; Dalton and Friend 2006; Rankinen et al. 2015; Zeevi et al. 2015). Risk factors were identified based on the participants' phenotypic and genetic features analyzed at the time of enrollment and were then used to offer 'actionable possibilities' for behavioral coaching (Predictive, Participatory) (Hood et al. 2015; Price et al. 2017). We analyzed the changes in each phenotype across the 3-month interval, both in the population as a whole and in the subgroup population. We found the phenotype change patterns for different interventions and the differences among each intervention, which provides new insights into improving health in a heterogeneous population with different needs for phenotypic improvement. In addition, we demonstrated how lifestyle interventions can be practiced correctly to achieve the best outcomes. Finally, we examined the impact of gene polymorphisms related to folic acid metabolism on the change of HCY during three months of this program.

Materials and Methods

Study Design

The study is a prospective, 3-month individualized intervention management and effect evaluation (Fig. 1a). At the time of enrollment, a baseline assessment was conducted for the participants two weeks before the intervention and management. The assessment included lifestyle questionnaires, clinical tests (Fasting plasma glucose (FPG), glycosylated hemoglobin, type A1C (HbA1C), total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and HCY), folate metabolism-related genes testing, metabolic testing (VD₃), measurement of height, weight, blood pressure. Then, nutritionists and doctors formulated for each individual a combination of health management suggestions including exercise, diet, and dietary supplement plans based on previous results. Recommended dietary supplements include deep-sea fish oil (contains eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and total omega-3 fatty acids), vitamin B complexes (contains calcium L-methyltetrahydrofolate, thiamine B₁, riboflavin B₂, nicotinic acid, vitamin B₆, vitamin B₁₂, biotin, pantothenic acid, choline, cyclohexanehexol), and VD₃ 5000 IU according to the participants' blood lipid level, HCY level, VD₃ level, genetic data, etc. Under the guidance of nutritionists, one or more dietary supplements are recommended. During the 3-month management, each participant was assigned a special health coach who communicated and guided him/her appropriately, and the participants were supposed to give feedback on the phenotypic changes at any time. In addition, individuals were followed up with a survey every week to confirm their nutritional supplement compliance. The actionable possibilities would be brought to the individuals by coaches who could clearly explain the actionable possibilities and how the individual could respond appropriately to them in the context of their desires for health. After the intervention, the phenotypic tests same as the baseline assessment were performed again for each participant by the same laboratory. The groups mentioned in the analysis were divided according to actual implementation after the intervention, without initial grouping.

Fig. 1.

The experimental pipeline and grouping methods. a A total of 350 individuals participated in the P350 project. Physical examination and clinical and genetic tests were performed two weeks before the intervention and data were collected. Afterward, each received health science education and a survey on family disease backgrounds, diet, medicine and supplements, daily activities, etc. Then each individual underwent a 3-month intervention with the guidance of the coach. At the end of the intervention, we performed the metabolic and phenotypic tests on all of them again. b After the intervention, participants were divided into groups SL (dietary supplements and lifestyle), group S (dietary supplements), and group L (lifestyle) according to each individual's actual implementation. There were 267 individuals in the SL group, 26 in the S group, and 55 people in the L group. Of the 267 in the SL group, a subset of participants who performed lifestyle interventions including diet and exercise was termed the D&E group (N = 193). The rest with either diet or exercise were classified as the D|E group (N = 74)

Inclusion and Exclusion Criteria

This study was approved by the ethics of Chinese People's Liberation Army (PLA) General Hospital (S2019-190-02). Participants are mental laborers from Beijing at high risk of metabolic syndromes. Subjects with the latest one of the following criteria were included in our study: (1) TC ≥ 5.7 mmol/L, (2) TG > 1.7 mmol/L, (3) LDL-C ≥ 3.37 mmol/L, (4) FPG ≥ 6.1 mmol/L, and (5) HCY ≥ 15 μmol/L. In this study, 350 people, mainly scientific researchers between the ages of 38 and 59, eventually completed the pipeline and post-intervention testing.

Exclusion criteria are: if any of the following, including type I diabetes mellitus, hereditary hyperlipidemia, those who have used insulin to lower blood sugar, abnormal liver and kidney function, mental illness, pregnancy or breastfeeding, malignant tumor, coronavirus disease 2019 (COVID-19) infection, physical disability or physical discomfort, requires bed rest for 50% or more of the day, severe or major surgery within six months, major organ lesions (including but not limited to the abnormal liver and kidney function, cerebral hemorrhage, cerebral infarction, cardiomyopathy, cardiac insufficiency, acute myocardial infarction, hypertensive emergency or hypertensive crisis, respiratory failure, pulmonary fibrosis, glaucoma, moderate or severe anemia, coagulation disease, thrombotic disease, severe electrolyte disturbances, etc.).

Physical Examination, Clinical Tests, and Genetic Tests

Before and after the intervention, we conducted a multi-dimensional and in-depth health assessment of the participants, including two physical examination parameters, 10 clinical markers, and three genetic markers.

At the same time, the physical examination data of the participants including body weight, and height were obtained through the physical examination center of the hospital (Beijing Zhongguancun Hospital). During the examination, fasting venous blood was collected in the morning to check the level of various markers, including FPG, HbA1C, TC, LDL-C, HDL-C, TG, HCY, VD₃, diastolic blood pressure (DBP), systolic blood pressure (SBP), etc. 5,10-methylenetetrahydrofolate reductase (MTHFR) and methionine synthase reductase (MTRR) gene polymorphisms were also analyzed. In addition, the body weight (kg) and height (m) were measured at the same time. BMI is calculated based on the results of weight and height measurements. The calculation formula is: $BMI=bodyweight(kg)/\left(h,e,i,g,h,t,\left(m\right)\right)2.$

Questionnaire

Under the guidance of professionally trained personnel, the health assessment and relevant data collection were completed in the form of questionnaires based on uniformly formulated physical examination standards. The collected information included gender, age, smoking status, alcohol consumption, lifestyle habits such as diet and exercise, etc. During the intervention, at 1-week intervals, participants were asked to fill out questionnaires about diet, exercise, and dietary supplement use. The dietary questionnaire asked on seven aspects whether the participants have: “reduced the use of sauces, salt, sugar”, “balanced the intake of meat and vegetables”, “reduced the amount of pro-inflammatory foods such as carbonated drinks, fried food, milk tea, bacon, etc.”, “increased the intake of nuts”, “increased the number of antioxidant foods such as red beans, blueberries, apples, broccoli, etc.”, “diversified the intake of vegetables and fruits”, “chosen healthy edible oils, such as flaxseed oil, olive oil, rapeseed oil”. The exercise questionnaire inquired on how the following three aspects were implemented: “adding a favorite type of exercise”, “increasing the time of exercise”, and “increasing the intensity of exercise”. The questionnaire on dietary supplement were: “How do you take dietary supplements?”, “How does your body improve after taking dietary supplements?”, “What discomfort does your body experience after taking dietary supplements?”, “Specify the location, level, and time of the discomfort”, “What measures have been taken to reduce the discomfort”, “What are the reasons for not taking dietary supplements as required?”, etc.

Methods of Intervention

Before the intervention, all participants were offered health education and expert Q&As on metabolic syndromes (Ghisi et al. 2020). According to the baseline phenotypes and genotypes assessment of each participant after enrollment, a personalized and precise intervention protocol was given by the physician team (Li et al. 2022). The intervention protocol included individualized dietary and exercise suggestions, precise dietary supplement advice and other healthy lifestyle advice including behavior management, stress management, and emotional relief techniques (Price et al. 2017; Sagner et al. 2017). Intervention protocol was performed under the supervision of coaches. The online form was used to collect information about daily exercise including duration, intensity, and frequency. Daily meal information is sent to the coaches via photographs. Some mistakes were also corrected in the course of supervision, such as both duration and intensity should be taken seriously in exercise, rather than just focusing on one of them. Some hard-to-notice unhealthy foods should also be avoided in diet, such as foods with sweeteners, trans fatty acids, preservatives, additives, and ultra-processed foods.

Evaluation During the Intervention

Follow-up assessments were performed weekly. To better guide, help, and support the participants to improve their wellness through self-management behaviors, a healthcare management group was established, and management projects such as health science education, expert Q&A, and exercise clock-in were carried out in the group every week. For various reasons including the COVID-19 outbreak, those who did not implement the intervention program as prescribed or did not perform post-intervention testing, or failed to respond after being contacted more than three times, were defined as dropouts.

Study Group

All participants were divided into three groups based on the actual implementation within three months. Two hundred and sixty-seven individuals who participated in both the lifestyle intervention and dietary supplements advice were defined as the supplements and lifestyle intervention group (SL). Twenty-eight participants who participated in only dietary supplements advice were in supplements intervention group (S). Fifty-five who performed only lifestyle interventions were set as the lifestyle intervention group (L) (Fig. 1b).

Based on the track record, we found that the 267 participants performed the lifestyle intervention differently in the SL group. Among them, 193 participants in the SL group performed both diet and exercise interventions (D&E); 74 participants in the SL group received either diet or exercise intervention (D|E) (Fig. 1b). Compared to the SL group (N = 267) included 193 D&E and 74 D|E (consists of 36 diet only and 38 exercise only). Group L (N = 55) only includes 12 D&E, three D|E (consists of 2 diet only, 1 exercise only) and 40 NA. Therefore, we performed subgroup analyses only in the SL group.

Definition of the Clinical Outcomes

Clinical outcomes are classified into three groups (stable, improved, and deteriorated) based on the phenotypic changes before and after the intervention. “Stable” outcomes mean the phenotypic indicators were within the normal range both before and after the intervention. Two types can be defined as “improved” outcomes: (1) the phenotype was abnormal before the intervention and returned to the normal range after the intervention. (2) The phenotype was abnormal before and after the intervention, but the change was towards the normal range. The “deteriorated” outcomes also included two categories: (1) the phenotype was normal before the intervention but showed abnormality after the intervention. (2) The phenotypes were within the abnormal range before and after the intervention but were even further deviated from the normal value.

The combination of statistical significance and clinical outcomes can give us a more comprehensive perspective of the intervention. For example, when the phenotypic changes were statistically significant, but the intervention outcome was stable, this means that the intervention was effective, but not necessarily clinically significant. A longer intervention or a more intensive program is necessary to bring about clinical significance.

Statistical Analysis

Data analysis was performed using IBM SPSS Statistics, Version 25.0. Numerical variables are expressed as mean ± standard deviation. To compare the data from 0 to 3 months, the Kolmogorov–Smirnov normality test was performed. Paired sample t-test was used for results following normal distribution, and the Wilcoxon test for two associated samples was used for those that do not. For comparison of changes between groups, data were subjected to single-factor ANOVA and post hoc test. For data following the homogeneity of variance, the Bonferroni post hoc test was used for two-way comparisons. Data distributions that do not fit the homogeneity of variances use the Kruskal–Wallis H post hoc test. p-value ≤ 0.05 was considered statistically significant. When regimens were compared pairwise, Bonferroni corrections were considered statistically significant to account for multiple testing.

We adjusted for the effects of age, sex, BMI, smoking, and drinking on the HCY level using multivariable logistic regression in 226 participants who took the vitamin B compounds (Table S2-3).

Results

An Overview of the Participants

A group of scientific researchers at high risk of metabolic syndrome including obesity and prediabetes, between the ages of 38 and 59, were recruited in Beijing for the proactive intervention study. Based on the criteria described in the materials and methods, 350 people eventually completed the pipeline and post-intervention testing was selected in this study which was named the P350. Before and after the intervention, we conducted a multi-dimensional and in-depth health assessment of the participants, including physical exams, clinical and genetic tests as described in the methods. Table 1 shows the characteristics of the participants among who no adverse events were observed or reported throughout the intervention protocol. Of the 11 phenotypic assessments, there was an average of four abnormalities per person. 77% percent of participants had three to nine abnormal phenotypes. The abnormal proportion of the cohort for each phenotype ranged from 10 to 89%, and the average phenotype abnormality rate reached 36% (Fig. 2a). 95% of the enrolled people carried MTHFR gene or MTRR gene mutations. For MTHFR A1298C, the proportion of alleles is AA: AC: CC = 65%: 32%: 3%; for MTHFR C677T, CC: CT: TT = 26%: 49%: 25%; for MTRR A66G, AA: AG: GG = 53%: 39%: 7% (Fig. 2b). Abnormal proportions of BMI, VD₃, TC, HCY, and LDL-C all exceeded 40% (Fig. 2c). Among them, the abnormal proportion of BMI reached 63%, the dyslipidemia population reached 69%, the HCY abnormal population reached 49%, and the VD₃ abnormal population reached 89%. However, the proportion of people with abnormal blood sugar and blood pressure is relatively low.

Table 1.

Demographics characteristics of the 350-person cohort

	Mean ± SD
N	350
Sex (m/f)	218/132
Age (years)	48.07 ± 8.20
Height (cm)	167.75 ± 8.24
BMI (kg/m2)	24.95 ± 3.30
Smokers	102
Alcoholics	108
MTHFR A1298C (wild-type/heterozygous/homozygous)	226/113/11
MTHFR C677T (wild-type/heterozygous/homozygous)	90/171/89
MTRR A66G (wild-type/heterozygous/homozygous	187/138/25
Omegacert	159
Complete B-Complex	226
Vitamin D3	198
DBP (mmHg)	80.09 ± 10.79
SBP (mmHg)	121.77 ± 14.16
TC (mmol/L)	5.29 ± 1.05
TG (mmol/L)	1.74 ± 0.97
LDL-C (mmol/L)	3.19 ± 0.83
HDL-C (mmol/L)	1.33 ± 0.33
HCY (μmol/L)	11.18 ± 6.07
VD3 (ng/mL)	20.12 ± 8.69

The basic information of all samples involved in this study, including lifestyle habits, genetic information, dietary supplement use, clinical markers, etc.

Fig. 2.

Overview of health status before and after the intervention in the overall cohort. a Proportions of participants with different levels of phenotypic features before the intervention. Blue, green, and red indicate that the expression level of markers is lower than the normal value, within the normal range, and higher than the normal value, respectively. b Percentage of all participants in the cohort with different MTHFR and MTRR gene polymorphisms. (Dark green: wild type; green: heterozygote; light green: homozygote. c The proportions of abnormal features at the time of enrollment for different phenotypic features. Percentages labeled in vertices are proportions of the abnormal population for phenotypic features. Note that blood lipids include TC, TG, HDL-C, and LDL-C. Blood glucose includes FPG and HbA1C. Scatter plots of levels of TC (d), LDL-C (e), HDL-C (f), HbA1C (g), HCY (h), and VD₃ (i) of participants before (0 months) and after (3 months) the intervention. The significance of the difference is shown above the scatter points (*p < 0.05, **p < 0.01) by the two-sided t-test (normal distribution) or Wilcoxon test (non-normal distribution). The lines in the middle represent the median, and the lines above and below represent the first and third quartiles

Longitudinal Comparisons of Phenotypic Features in the P350 Cohort

We estimated changes in nine key phenotypic features in the P350 cohort over three months. A total of six features showed statistically significant improvement based on differential analysis (Fig. 2d–i, Table 2). Three blood lipids markers, including TC, LDL-C, and HDL-C, one blood glucose marker which is HbA1C, HCY, and VD₃ had improvements in the entire population. Analysis of the clinical outcomes of these significantly altered markers found that four of them changed within the normal range, which we categorized as “stable” in the methods. They were TC, LDL-C, and HbA1C which were significantly decreased as well as HDL-C which was significantly increased in the normal range. The rest two markers changed from abnormal to normal and fell into the “improved” category, including HCY which decreased significantly from an abnormally high value to the normal range, and VD₃ which increased significantly from a low value to the normal range.

Table 2.

Changes of phenotypes in a cohort of 350 individuals after intervention

Markers	Mean ± SD changes in 3 months		Marker status changes in 3 months		Changes direction	p
	0 month	3 month	0 month	3 month
BMI (kg/m2)	24.95 ± 3.30	24.83 ± 3.26	High	High	↓
TC (mmol/L)	5.29 ± 1.06	5.05 ± 1.00	Normal	Nomal	↓	< 0.001
TG (mmol/L)	1.74 ± 0.97	1.75 ± 1.11	High	High	↑
LDL-C (mmol/L)	3.19 ± 0.83	3.05 ± 0.83	Normal	Nomal	↓	< 0.001
HDL-C (mmol/L)	1.33 ± 0.33	1.34 ± 0.30	Normal	Nomal	↑	0.028
FPG (mmol/L)	5.41 ± 0.93	5.45 ± 0.88	Normal	Nomal	↑
HbA1C (%)	5.81 ± 0.60	5.77 ± 0.52	Normal	Nomal	↓	0.037
HCY (μmol/L)	11.23 ± 6.11	8.89 ± 2.93	High	Normal	↓	< 0.001
VD3 (ng/mL)	19.98 ± 8.64	30.37 ± 13.29	Low	Normal	↑	< 0.001

Marker states are divided into high, normal, and low, which mean that marker levels are above the normal range, within the normal range, and below the normal range, respectively. Changing direction implies a trend in marker levels at 3 month relative to 0 month

↑ Relative to 0 month, the marker level rises after three months

↓ Relative to 0 month, the marker level decreases after three months

p-value < 0.05 is shown in the table. The blank cell indicates that the marker has not changed statistically significantly within three months of the corresponding group. Bonferroni post hoc test was used for two-way comparisons in data following the homogeneity of variance. Kruskal–Wallis H post hoc test was used for two-way comparisons in data do not fit the homogeneity of variances

Overall, among the six statistically significant changed markers, three indicate blood lipids and one is relevant to blood glucose fluctuating within the normal range, while HCY and VD₃ experienced clinical outcome improvements after the intervention. Next, we checked the patterns of phenotypic changes induced by specific interventions.

Longitudinal Changes and Cross-Section Comparisons Revealed Different Intervention Outcomes of SL, S, and L

Different Phenotypic Change Patterns of Different Intervention Groups

We compared nine key phenotypic features of participants who intervened with dietary supplements + lifestyle, dietary supplements only, and Lifestyle only (Fig. 3a–m, Tables 3 and 4) and found that the intervention effect of the SL group is more similar to that of the S than to the L group. Both SL (p_TC < 0.001; p_FPG = 0.001; p_HCY < 0.001, $p_{{\text{VD}}_3}$ < 0.001) and S (p_TC = 0.007; p_FPG = 0.035; p_HCY < 0.001, $p_{{\text{VD}}_3}$ < 0.001) groups had significant improvement in TC, HCY, and VD₃ levels, suggesting a reasonable combination of dietary supplements can effectively modulate TC, HCY, and VD₃ levels in individuals with metabolic diseases in as short as three months. FPG increased significantly in both SL and S groups but decreased significantly in the L group. But the average level of FPG remained within the normal range before and after the intervention in the SL/S/L group, so it is not clinically meaningful. Additionally, HDL-C increased significantly only in the S group (1.35 ± 0.25 mmol/L vs. 1.43 ± 0.36 mmol/L; p_S = 0.012). SL group was the only group with a significant decrease in LDL-C (3.22 ± 0.84 mmol/L vs. 3.11 ± 0.88 mmol/L; p_SL < 0.001). BMI value significant decrease in the normal range only in the L group, but not in other intervention groups.

Fig. 3.

Levels of phenotypic features before and after the intervention in the SL, S, and L groups as well as the relationship between folate metabolism-related genes and HCY level changes. Scatter plots for the levels of TC (a), LDL-C (b), FPG (c), HCY (d), and VD₃ (e) of participants of the SL group, levels of TC (f), HDL-C (g), FPG (h), HCY (i), and VD₃ (j) of participants of the S group and values of BMI (k), FPG (l), and VD₃ (m) of participants of the L group before (0 month) and after (3 month) the intervention. n HCY changes by the intervention for individuals with different MTHFR C677T polymorphisms. The numbers of participants of the three MTHFR C677T polymorphisms are homozygotes (TT, N = 63), heterozygotes (CT, N = 117), and wild type (CC, N = 46). o HCY changes by the intervention for individuals with different folic acid utilization abilities based on MTRR A66G, MTHFR C677T, and A1298C polymorphisms. The WEAKER group (N = 80) contained at least one homozygote for the three gene polymorphisms, and the population of the STRONGER group (N = 146) contained no homozygous mutations in the three gene polymorphisms. The line in the middle represents the median, and the lines above and below represent the first and third quartiles. Results of pairwise comparisons of zero and three months by two-sided t-test (normal distribution) or Wilcoxon test (non-normal distribution) were presented above the scatter points with *p < 0.05, and **p < 0.01. When comparing three groups in panel n, two-sided single-factor ANOVA (following the homogeneity of variance) or Kruskal–Wallis H test (not following the homogeneity of variance) was used

Table 3.

Changes of phenotypes in the each group after the intervention

Markers	Group	Mean ± SD changes in 3 months		Marker status changes in 3 months		Changes direction	p
		0 month	3 month	0 month	3 month
BMI (kg/m2)	SL	24.83 ± 3.43	24.98 ± 3.46	High	High	↑
	S	25.95 ± 2.73	25.89 ± 2.42	High	High	↓
	L	25.05 ± 3.32	24.44 ± 3.15	High	High	↓	< 0.001
TC (mmol/L)	SL	5.36 ± 1.09	5.15 ± 1.07	Normal	Nomal	↓	< 0.001
	S	5.39 ± 1.00	5.13 ± 0.99	Normal	Normal	↓	0.007
	L	4.99 ± 1.08	4.83 ± 1.00	Normal	Normal	↓
TG (mmol/L)	SL	1.76 ± 0.95	1.70 ± 0.92	High	High	↓
	S	1.67 ± 0.73	1.54 ± 1.10	Normal	Normal	↓
	L	1.76 ± 1.08	1.84 ± 1.43	High	High	↑
LDL-C (mmol/L)	SL	3.22 ± 0.84	3.11 ± 0.88	Normal	Normal	↓	< 0.001
	S	3.15 ± 0.72	3.07 ± 0.79	Normal	Normal	↓
	L	3.05 ± 0.84	3.00 ± 0.84	High	High	↓
HDL-C (mmol/L)	SL	1.34 ± 0.36	1.34 ± 0.32	Normal	Normal	–
	S	1.35 ± 0.25	1.43 ± 0.36	Normal	Normal	↑	0.012
	L	1.26 ± 0.31	1.28 ± 0.25	High	High	↑
FPG (mmol/L)	SL	5.05 ± 0.79	5.23 ± 0.71	Normal	Normal	↑	0.001
	S	5.20 ± 0.74	5.40 ± 0.93	Normal	Normal	↑	0.035
	L	6.00 ± 0.63	5.68 ± 0.78	Normal	Normal	↓	0.002
HbA1C (%)	SL	5.68 ± 0.5	5.66 ± 0.47	Normal	Normal	↓
	S	5.81 ± 0.63	5.81 ± 0.65	Normal	Normal	–
	L	5.88 ± 0.60	5.85 ± 0.45	Normal	Normal	↓
HCY (μmol/L)	SL	11.43 ± 6.21	8.30 ± 2.48	High	Normal	↓	< 0.001
	S	12.40 ± 7.14	8.97 ± 2.18	High	Normal	↓	< 0.001
	L	11.73 ± 5.03	10.80 ± 3.19	High	High	↓
VD3 (ng/mL)	SL	19.66 ± 5.83	34.82 ± 13.14	Low	Normal	↑	< 0.001
	S	20.72 ± 6.28	32.53 ± 13.69	Low	Normal	↑	< 0.001
	L	15.53 ± 6.66	19.78 ± 8.36	Low	Low	↑	< 0.001

SL groups, S groups, and L groups are shown separately. Marker states are divided into high, normal, and low, which mean that marker levels are above the normal range, within the normal range, and below the normal range, respectively. Changing direction implies a trend in marker levels at 3 month relative to 0 month

↑ Relative to 0 months, the marker level rises after three months

↓ Relative to 0 months, the marker level decreases after three months

–The marker remained within normal ranges both in 0 month and 3 month

p-value < 0.05 is shown in the table. The blank cell indicates that the marker has not changed statistically significantly within three months of the corresponding group. Bonferroni post hoc test was used for two-way comparisons in data following the homogeneity of variance. Kruskal–Wallis H post hoc test was used for two-way comparisons in data do not fit the homogeneity of variances

Table 4.

Significantly changed phenotypes in each group

	BMI	TC	TG	LDL-C	HDL-C	FPG	HbA1C	HCY	VD3
SL		↓		↓		↑		↓	↑
S		↓			↑	↑		↓	↑
L	↓					↓			↑

Markers of significant change within 3 months in the corresponding group were marked with the change direction. The blank cell indicates that the marker has not changed statistically significantly within 3 months of the corresponding group

↑ Relative to 0 months, the marker level statistically significantly rises after three months

↓ Relative to 0 months, the marker level statistically significantly decreases after three months

In summary, the results above showed abnormal phenotypic markers with values far from the normal range before the intervention, including HCY and VD₃, which were significantly improved after the intervention. Other abnormal markers slightly different from the normal values or at the edges of the normal range at the time of enrollment, including BMI, TC, and LDL-C, were also improved significantly in one or two intervention groups after intervention. HDL-C is an anti-atherosclerotic plasma lipoprotein and a protective factor for coronary heart disease, commonly known as a “vascular scavenger”. HDL-C levels of most participants were within the normal range before the intervention and increased significantly but within the normal range after the intervention, towards better health.

Overall, different intervention groups (SL\S\L groups) showed varying phenotypic change patterns. Five phenotypic feature changes in the SL group were particularly significant (p < 0.01), three changes in the S group were particularly significant (p < 0.01), and two were significant (p < 0.05). Three phenotypic changes in the L group were particularly significant (p < 0.01). In conclusion, dietary supplements are undoubtedly superior to lifestyle interventions in terms of improving the level of markers faster and greater.

Pairwise Comparisons of Changes of Features Among Different Intervention Groups

The amount of change of each feature before and after the intervention (Δvalue = after-before) was compared using ANOVA and post hoc tests among SL, S, and L groups as in the methods. ΔBMI of the L group was significantly different from the SL and S groups (Fig. 4a, Table 3) (ΔBMI_SL = 0.14 ± 0.96 kg/m², ΔBMI_S = − 0.06 ± 0.95 kg/m², ΔBMI_L = − 0.61 ± 1.09 kg/m², p_{SL vs. L} = 0.001, p_{SL vs. S} = 0.042). ΔFPG was statistically different (Fig. 4b, Table 3) (p_ΔFPG = 0.000) between SL and L (ΔFPG_SL = 0.19 ± 0.57 mmol/L, ΔFPG_L = − 0.32 ± 0.74 mmol/L, p_{SL vs. L} < 0.001), between S and L (ΔFPG_S = 0.20 ± 0.55 mmol/L, ΔFPG_L = − 0.32 ± 0.74 mmol/L, p_{S vs. L} = 0.002). ΔHCY was statistically different (Fig. 4c, Table 3) (p_ΔHCY = 0.029) between SL and L (ΔHCY_SL = − 3.13 ± 6.01 μmol/L, ΔHCY_L = − 0.93 ± 4.72 μmol/L, p_{SL vs. L} = 0.037). No statistically significant difference was found between groups for other phenotypic markers.

Fig. 4.

Differences in the level changes of phenotypic features after the intervention among groups. Scatter plots for the changes in plasma levels of BMI (a), FPG (b), and HCY (c) of participants in the SL, S, and L groups that numbers of participants in each group are: SL (N = 267), S (N = 28), and L (N = 55). When dividing the participants in the SL group into D&E groups who performed both diet and exercise interventions and D|E groups who received either diet or exercise intervention, the changes in BMI (d), FPG (e), and HbA1C (f) levels were plotted. The line in the middle represents the median, and the lines above and below represent the first and third quartiles. Two-sided single-factor ANOVA (following the homogeneity of variance) or Kruskal–Wallis H test (not following the homogeneity of variance) was used and p-values are: *p < 0.05, and **p < 0.01

From the above analysis, we can find that phenotypic changes were statistically significantly different between the SL and L groups, as well as the S and L groups, but no difference was found between the SL and S groups, indicating that the outcomes of the SL and S groups were more similar. That is, people who took dietary supplements were more similar than those who did not. To test this conjecture, we combined the SL and S groups and compared them with the L group to explain the effect of taking dietary supplements on each phenotype. Interestingly, we found again only significant changes in ΔBMI, ΔFPG, and ΔHCY in the group pairwise comparisons, which were completely consistent with the above results (Fig. S1a–c). The p-values for the comparisons between the two groups were: p_ΔBMI = 0.000, p_ΔFPG = 0.000, and p_ΔHCY = 0.009. In summary, the effect of lifestyle alone on HCY was limited (Fig. 4c). Compared with the L group, HCY decreased statistically significantly more in the SL group, which was also supported by the comparison of the combination group (SL + S) with L (Fig. S1c). Therefore, dietary supplementation is more effective in reducing HCY. BMI and FPG are affected by both dietary supplements and lifestyle (Fig. 4a, b). However, there was confounding in lifestyle adherence in the SL group (discussed in Groups D&E and D|E), so the effects of dietary supplements on BMI and FPG need to be further investigated.

In this part of the work, we first identified statistically significantly altered phenotypes within each intervention group by longitudinal comparison. Further, through the cross-section comparison between groups, we concluded that dietary supplementation is more effective in reducing HCY, while the effects of dietary supplements on BMI and FPG need to be further investigated.

A Combination of Diet and Exercise is Necessary to Improve Wellness

The lifestyle intervention included both diet and exercise advice. From the questionnaires, we noticed that in the SL group, the compliance of 267 participants to the given advice was different. One hundred and ninety-three participants who followed the diet and exercise suggestions strictly were defined as the D&E group. The rest 74 participants who performed either diet or exercise intervention alone were set as the D|E group. The analysis results showed that ΔBMI (ΔBMI_D&E = −0.13 ± 1.04 kg/m², ΔBMI_D|E = 0.26 ± 0.92 kg/m², p_ΔBMI = 0.008), ΔTG (ΔTG_D&E = −0.05 ± 0.85 mmol/L, ΔTG_D|E = −0.21 ± 0.87 mmol/L, p_ΔTG = 0.018) and ΔHbA1C (ΔHbA1C_D&E = −0.06 ± 0.30%, ΔHbA1C_D|E = 0.00 ± 0.30%, p_ΔHbA1C = 0.044) were significantly different between the D&E group and the D|E group (Fig. 4d–f). ΔBMI, ΔTG and ΔHbA1C decreased only in the D&E group. However, in the D|E group, ΔBMI and ΔTG even increased, and ΔHbA1C remained unchanged. Therefore, a combination of a healthy diet and exercise is necessary to reduce BMI, TG, and HbA1C. This result potentially suggests that the strange phenomenon of elevated ΔBMI in the SL group may be due to participants failing to fully comply with recommendations for lifestyle changes.

Effect of Folate Metabolism-Related Gene Polymorphisms on Longitudinal Changes of HCY

MTHFR and MTRR are the key enzymes in the methionine-folate metabolism system, which play an important role in maintaining the normal metabolism of folic acid (Kim et al. 2011). The polymorphisms of MTHFR gene C677T, A1298C site, and MTRR gene A66G site can affect the activities of MTHFR and MTRR enzymes, resulting in abnormal folic acid metabolism (Liew and Das Gupta 2015; Wei et al. 2015). HCY is remethylated and metabolized by the folate pathway. Multiple works have performed genome-wide association studies (GWASs) in different ethnic groups, confirming that the gene polymorphisms are significantly associated with total HCY levels (Hazra et al. 2009; Kim et al. 2016; Lange et al. 2010; Malarstig et al. 2009; Pare et al. 2009; Raffield et al. 2018; Shane et al. 2018; Souto et al. 2005; Tanaka et al. 2009; van Meurs et al. 2013; Wernimont et al. 2011; Williams et al. 2014). The polymorphism of MTHFR C677T showed a large effect on total HCY levels (Liew and Gupta 2015; Moll and Varga 2015). Compared with the wild-type carriers, individuals with homozygote T alleles had an average of 31.08% increased total HCY levels (Shane et al. 2018). This association was validated in the 5175 Chinese population (Liu et al. 2021). Overall, folate metabolism-related gene deficiency leads to elevated HCY and low folate and is associated with an increased risk of multiple diseases (Goyette et al. 1994). In this study, we validated the association of folate metabolism-related genes polymorphisms, folic acid, and the level of HCY.

MTHFR and MTRR Polymorphism of the Participants and Different Intervention Outcomes

First, we analyzed the most widely reported MTHFR C677T locus of the 226 participants. ΔHCY levels before and after the intervention of the three populations with different alleles which were the wild type (CC, N = 46), heterozygotes (CT, N = 117), and homozygotes (TT, N = 63) were compared pairwise. As expected, ΔHCY showed a significant difference among the three groups (p_ΔHCY = 0.000) (Fig. 3n). After Bonferroni correction, it was found that ΔHCY decreased more in the TT group than in the CT group (ΔHCY_TT = − 6.60 ± 10.23 μmol/L, ΔHCY_CT = − 1.63 ± 2.50 μmol/L, p_{TT vs. CT} < 0.001) and CC group (ΔHCY_TT = − 6.60 ± 10.23 μmol/L, ΔHCY_CC = − 1.64 ± 2.16 μmol/L, p_{TT vs. CC} = 0.010), the difference was significant. It is worth noting that the average level of HCY in the CC group was normal before and after the intervention (HCY_CC before = 9.30 ± 2.87 μmol/L, HCY_CC after = 7.66 ± 2.05 μmol/L). The mean HCY level of the CT group was close to abnormal before the intervention and returned to normal after the intervention (HCY_CT before = 9.79 ± 2.81 μmol/L, HCY_CT after = 8.16 ± 2.27 μmol/L). The average level of HCY in the TT subtype population was at a high value before the intervention and returned to the normal level after the intervention (HCY_TT before = 15.23 ± 10.10 μmol/L, HCY_TT after = 8.62 ± 3.72 μmol/L).

According to the guidelines issued by the Chinese Center for Disease Control and Prevention, MTHFR C677T, A1298C, and MTRR A66G gene polymorphisms were classified into four categories which correlate with normal, slightly weak, relatively weak, and weak abilities to utilize folic acid (Table S1). The numbers of the participants who fell into each category were: normal (N = 13), slightly weak (N = 133), relatively weak (N = 74), and weak (N = 6). Since the number of “normal” and “weak” groups is relatively small, we combined participants with “normal” and “slightly weak” folate utilization capacity (STRONGER) into a group (N = 146), and participants with “relatively weak” and “weak” folate utilization capacity (WEAKER) into another group (N = 80). WEAKER group contained at least one homozygote for the three gene polymorphisms, and the STRONGER group contained no homozygous mutations in the three gene polymorphisms. ΔHCY exhibited significant differences between the two groups (Fig. 3o), with mean HCY higher in the weaker group than in the stronger group before intervention (WEAKER before = 13.98 ± 9.33 μmol/L, WEAKER after = 8.49 ± 3.44 μmol/L; STRONGER before = 9.68 ± 2.86 μmol/L, STRONGER after = 8.02 ± 2.23 μmol/L). A greater reduction was shown in the weaker group (ΔHCY_WEAKER = − 5.50 ± 9.38 μmol/L, ΔHCY_STRONGER = − 1.66 ± 2.40 μmol/L, p_{WEAKER vs. STRONGER} = 0.003), again showing the effectiveness of the intervention.

Active Participation and Influence on the Participants by the P350 Project

During the 3-month health management, most participants showed remarkable compliance with the P350 project. This is demonstrated by the data that 59.14% of the participants improved at least one phenotypic feature at the end with an average improvement of one phenotype per person (Fig. 5a). Features of 18.86% of the participants remained stable with the clinical outcomes, but values may improve statistically as addressed above. Figure 5b shows the percentage of phenotypic improvement from abnormal to normal. For example, VD₃ improved in 81% of participants with abnormal VD₃ enrollment; HCY improved in 87% of participants with abnormal HCY enrollment; TC and LDL-C improved in 80% and 65% of those with abnormal enrollment, respectively; 65% of those with abnormal TG enrollment improved; and 76% of those with abnormal HDL enrollment improved. Both blood pressure and blood sugar improved in more than 70% of participants with abnormal enrollment. In particular, HbA1C improved in 91% of participants with abnormal enrollment. BMI improved in 61% of participants with abnormal enrollment. Overall, the precise nutritional intervention program played a key role in helping these phenotypes show a good trend of improvement in just three months. Equally important, because the intervention period is only three months, some effects still need longer term observation to be more truly reflected, especially the lifestyle intervention.

Fig. 5.

Overview of the intervention effects of the cohort. a Numbers of improved phenotypes in the post-intervention cohort (negative numbers represent deterioration). The green gradient part of the pie chart represents the proportion of the total population who improved by one phenotype marker or more, and the percentage of the improved population reached 59.14%. The more phenotypes that improve, the greener it becomes. The red gradient part of the pie chart represents the proportion of the total population with worsening of one phenotype marker or more, and the percentage of the worsening population is 22.00%. The more deteriorating phenotypes, the redder it becomes. The gray part in of the pie chart represents the proportion of the total population with neither improvement nor deterioration, and the percentage of the stable population is only 18.86%. b Histogram showing the proportion of each phenotypic feature improved from abnormal to normal

We came to three insights from this study:

• Phenome-based individualized interventions are emerging in China. The awareness of self-health management of the public, especially mental laborers, is awakening. A wellness plan based on phenotypic features can be targeted and actionable in daily life.

• In many situations, genes may impact the potential for the disease. Appropriate early interventions can effectively slow down the deterioration of phenotypic markers caused by gene deficiency.

• This study demonstrates the effectiveness of proactive intervention in improving synthetic phenotypes in the high-risk metabolic syndrome population and may contribute to public cognition (Schussler-Fiorenza Rose et al. 2019).

Discussion

Although there is strong scientific interest in using multi-dimensional data to study interventions in people with high-risk metabolic syndromes, few guided practices in the real world have been reported. Here, we presented a study to investigate a personalized intervention model that is easy to practice in daily life. We aimed to propose a model to spur the paradigm shift to P4 medicine, leading to more systematic personalized intervention methods, and helping maintain wellness and prolong the health span.

Our findings show that proactive precision interventions can have a positive impact on phenotypic markers. Previous studies also have shown the effectiveness of lifestyle interventions and dietary supplement interventions for phenotypic improvement. Lifestyle interventions can improve the phenotypes such as BMI, TC, LDL-C, HDL-C, FPG, and HbA1C in people with metabolic disorders (Khaled et al. 2006; Li et al. 2016; Liang et al. 2021; Omar et al. 2021; Qadir et al. 2021; Zhang et al. 2017; Zhou et al. 2022). Although there was no evidence that lifestyle interventions alone improved HCY and VD₃ levels, the L group in our study showed significant improvements in VD₃ levels after three months of intervention. VD₃ was the only marker that was significantly improved in all intervention groups, indicating that the regulation of human VD₃ levels can be achieved not only by dietary supplements but also by lifestyle intervention which increases exposure to sunlight during exercise. However, the HCY in group L did not decrease significantly as in groups SL and S, indicating that lifestyle intervention alone had little effect on the level of HCY. Omega-3 contained in fish oil can also significantly improve TC, HDL-C, FPG, and HbA1C (Wang et al. 2019; Xiao et al. 2022). Meta-analysis shows that EPA supplementation decreased the serum levels of TC and LDL-C, while DHA increased the serum levels of TC, LDL-C and HDL-C (Liang et al. 2021). The stable calcium salt of L-methyltetrahydrofolate acid contained in vitamin B complexes is the most active form of reduced folate circulating in plasma, which directly enters the metabolic process of folate (Seremak-Mrozikiewicz 2013). Intake of L-methyltetrahydrofolate may have advantages over the intake of folic acid (Pietrzik et al. 2010). Treatment with folic acid (Piazzolla et al. 2019) and vitamin B₁₂ (Petronijevic et al. 2008) are effective in reducing HCY levels. A previous study found that folate and 25-OH-VD levels were strongly associated with the risk of metabolic syndrome. 25-OH-VD deficiency has been linked to several metabolic syndrome risk factors, including increased serum TC and HbA1C (Lally et al. 2016), and to an increased metabolic risk (Bruins et al. 2018). This evidence supports the findings in this study that dietary supplements can help improve some phenotypes that cannot be easily changed by lifestyle, such as the rapid recovery of HCY to normal levels. However, lifestyle interventions had the best effect on BMI improvement. These results validate the feasibility and efficacy of combining dietary supplements and lifestyle interventions in people at high risk of metabolic syndrome.

We found that dietary supplementation is more effective in reducing HCY. Previous studies support that HCY levels are mainly affected by dietary supplements (Petronijevic et al. 2008; Piazzolla et al. 2019), which is consistent with our findings. As the results are shown above, the reductions in ΔBMI and ΔFPG were significantly greater in the L group than in the S group, which points to lifestyle's potential for improving BMI and FPG. Some studies show that both lifestyle and dietary supplements can affect FPG (Li et al. 2016; Qadir et al. 2021; Wang et al. 2019; Zhou et al. 2022). BMI is mainly influenced by lifestyle (Li et al. 2016; Liang et al. 2021; Omar et al. 2021; Zhou et al. 2022). However, due to limitations in our study design, we were unable to obtain the effects of dietary supplements on BMI and FPG. Deeper exploration could be carried out in future studies. These results present possibilities for precise intervention. Such models of intervention and analysis may provide deeper insights into precision medicine or reveal interesting biological implications.

Our results showed that a combination of diet and exercise is necessary to improve wellness. It has been proven by previous studies that diet–exercise interventions had larger improvement effects than diet or exercise-alone interventions, especially in improving body weight and TG (Atakan et al. 2021; Campbell et al. 2012; Zhang et al. 2017). These results are consistent with our study. Not only that, diet-exercise decreased biological age more than exercise, diet, or control (Ho et al. 2022). The P350 study necessarily includes its limitations. While we did closely supervise and advise participants to achieve better intervention outcomes, we observed only modest compliance. These interventions are only part of precision interventions, and more combinations of interventions could be tested in larger studies in the future.

Conclusion

The P350 project sets up a good paradigm that utilizes P4 medicine in the area of personal health management with the integration of information from genomics and phenome-based data. The study obtained the desired intervention effect and achieved a maximum improvement rate of 91% in the abnormal population. We found special advantages of dietary supplement intervention in recovering HCY levels. Notably, incomplete lifestyle interventions were not effective in controlling BMI, TG, and HbA1C levels, suggesting that a combination of diet and exercise is necessary to improve wellness and follow-up assessments are important to monitor the intervention outcome. Finally, we also verified that the population with mutations in folate metabolism-related genes was more likely to improve the HCY level through vitamin B supplementation.

Our results show that assessment of personal genotype and phenotype data can improve our understanding of the intervention method for high-risk metabolic syndrome, leading to more precise and personalized interventions. In the future, the increasing number of omics and personal dynamic data may contribute to an in-depth study of the association between genotype and phenotype, leading to a more comprehensive personalized intervention approach, and ultimately improved individual and population health.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

PLA

People's liberation army

P4

Predictive, preventive, personalized, and participatory

P350

Pioneer 350 wellness project

HCY

Homocysteine

VD₃

Vitamin D₃

BMI

Body mass index

P100

Pioneer 100 wellness project

PD3

Personal, dense, dynamic data

FPG

Fasting plasma glucose

HbA1C

Glycosylated hemoglobin, type A1C

TC

Total cholesterol

TG

Triglyceride

LDL-C

Low-density lipoprotein cholesterol

HDL-C

High-density leptin cholesterol

COVID-19

Coronavirus disease 2019

DBP

Diastolic blood pressure

SBP

Systolic blood pressure

MTHFR

5,10-Methylenetetrahydrofolate reductase

MTRR

Methionine synthase reductase

SL

Supplements and lifestyle intervention group

S

Supplements intervention group

L

Lifestyle intervention group

GWASs

Genome-wide association studies

D&E

Participants in the SL group who performed both diet and exercise interventions

D|E

Participants in the SL group who received either diet or exercise intervention

STRONGER

Participants with “normal” and “slightly weak” folate utilization capacity

WEAKER

Participants with “relatively weak” and “weak” folate utilization capacity

EPA

Eicosapentaenoic acid

DHA

Docosahexaenoic acid

Authors' Contributions

ZHu, XMao, QHuang, QZheng, WLv, LHood, FWang and FWu conceived and supervised the project. ZHu, XMao, DFu and CLu enrolled the participants. QHuang designed the analytical approach and performed data analysis. QHuang, ZHu, QZeng, QFang and CZeng edited the manuscript critically. All authors contributed to have approved the final manuscript.

Data Availability

The datasets of the current study are available from the corresponding author upon reasonable request.

Declarations

Conflict of interest

There is no conflict of interest regarding the publication of this paper. Leroy Hood and Zhiyuan Hu are the Editorial Board Members of Phenomics, and they were not involved in reviewing this paper.

Ethical Approval

This study was approved by the ethics of Chinese PLA General Hospital (S2019-190-02).

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Consent to Publish

All presentations have consent for publication.