Impact of a 12-week personalized dietary intervention on vascular function and cardiovascular risk factors

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Given the interaction between insulin resistance (IR) and cardiovascular risk, we examined whether a personalized diet according muscle insulin-resistant (MIR) or liver insulin-resistant (LIR) phenotypes improves vascular function and cardiovascular disease risk factors. Methods. Individuals were randomized to a personalized phenotype diet (PhenoDiet) A or B and followed a 12-week low-fat, high-protein (LFHP) diet or high-monounsaturated fatty acid (HMUFA) diet (PhenoDiet A; MIR/HMUFA-LIR/LFHP; PhenoDiet B: MIR/LFHP-LIR/HMUFA). We included 101 participants. Results. Dietary interventions decreased blood pressure, total cholesterol, HDL-cholesterol and the Framingham risk score (all P<0.05), improved IR ((Matsuda index, HOMA-IR) P<0.001), but not vascular function (P=0.485). Changes in outcome parameters were not significantly different between PhenoDiet groups. The LFHP diet resulted in more pronounced improvements in cholesterol, DBP, and IR compared to the HMUFA diet (all P<0.05). Conclusion. A 12-week healthy diet improves metabolic and cardiovascular outcomes, but not vascular function in IR adults with overweight or obesity. Whilst the LFHP diet resulted in greater improvements in cardiometabolic risk markers than the HMUFA diet, we found no significant differences between the PhenoDiet groups. Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity Health sciences/Endocrinology/Endocrine system and metabolic diseases/Pre-diabetes nutrition diet cardiovascular insulin resistance Figures Figure 1 What is already known? Dietary interventions have shown promising results for improving insulin resistance and vascular function Recent literature suggests that the success of a diet might be related to the metabolic phenotype of an individual; which can be predominantly in the muscle (MIR) or liver (LIR). Whether optimizing the diet to improve glucose homeostasis may translate into superior effects on cardiovascular risk factors is currently unknown. What are the new findings? A healthy diet for 12 weeks in insulin resistant individuals with obesity or overweight improved cardiovascular risk factors and insulin resistance. Personalized diets according to insulin resistance phenotype showed that the Matsuda index and total cholesterol tended to improve more, while no superiority was found for other outcomes. A low fat-high protein diet resulted in greater improvements in cardiovascular risk factors and measures of IR, compared to high-monounsaturated fatty acid diet. How do the results change clinical practice? Our results highlight the benefits of a healthy diet to improve cardiovascular risk and insulin resistance in individuals with insulin resistance who are obese or overweight Our study suggests that the diet type per se , which superiority of a low fat-high protein diet over high-monounsaturated fatty acid diet, to have larger effects compared to personalization of the diet based on the IR phenotype. Introduction The rising prevalence of obesity in the Western world is a major risk factor for insulin resistance (IR), and ultimately type 2 diabetes (T2D) and cardiovascular disease (CVD) [1]. Besides its metabolic role in glucose metabolism, insulin also acts on the vasculature [2]. Especially in the postprandial state, IR is characterized by hyperglycemia, hyperinsulinemia, lipotoxicity, oxidative stress, and inflammation. These metabolic aberrations are also associated with the inhibition of insulin-stimulated production of NO, increased ET-1 release and increased expression of vascular adhesion molecules in the endothelium, contributing to endothelial dysfunction and development of atherosclerosis [2]. Increased production of reactive oxygen/nitrogen species further reduces the availability of NO. Consequently, impairments in vascular function can worsen perturbations in glucose homeostasis by reducing blood flow and delivery of insulin and glucose to the tissues [2]. Dietary interventions have shown promising results for improving IR [3]. Due to the close relationship between IR and vascular dysfunction, it has been suggested that interventions that improve IR will also improve vascular function, and vice versa [4]. For instance, the Mediterranean diet seems beneficial for both, IR and vascular function [5]. Interestingly, recent literature suggests that the success of a diet in improving glucose homeostasis might be related to the metabolic phenotype of an individual [6]. While IR can affect the liver and skeletal muscle simultaneously, it can also be localized predominantly in the muscle (MIR) or liver (LIR) [6,7]. Studies show that the LIR- or MIR-phenotypes are associated with distinct lipidome [8], metabolome [9], and adipose tissue inflammatory transcriptome and systemic inflammatory profiles [10]. Similarly, the IR-phenotype may determine its effect to dietary interventions [6]. Long-term adherence to a Mediterranean diet has previously been shown to be more beneficial for those with MIR, and a low-fat, high complex carbohydrate diet for those with LIR, with respect to glucose homeostasis [6]. Similarly, high protein diets have been shown to reduce liver fat and inflammation in individuals with type 2 diabetes mellitus [11]. Thus, a low-fat, high-protein, high-fiber diet (LFHP) may be an optimal diet for individuals with LIR, while a Mediterranean-type diet (high in monounsaturated fat, HMUFA) may have more beneficial effects for individuals with more pronounced MIR [12]. Since IR and vascular (dys)function are tightly linked [2,13], optimizing the diet to improve glucose homeostasis may translate into superior effects on cardiovascular risk factors as well. To our knowledge, this is the first study to examine whether a more personalized diet to improve glucose homeostasis would also translate to optimal effects on vascular function in individuals with IR. The aim of this study was to determine the effect of a 12-week, more personalized dietary intervention on vascular function and CVD risk factors in individuals with either MIR or LIR. Methods Study population This current study was executed within the framework of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) study [12] and includes tissue-specific insulin resistant (MIR or LIR), weight stable (3 months ≤3 kg weight gain/loss) individuals (age 40-75), with a BMI between 25-40 kg/m 2 . Main exclusion criteria were: pre-diagnosed diabetes type 2, glucose/lipid altering medications, uncontrolled hypertension, alcohol consumption >14 units/week, smoking, and moderate-to-vigorous physical activity (MVPA) >4 hours/week.A table with all exclusion criteria can be found elsewhere in the design paper of the study [12]. In total, 119 participants were included. During the intervention, 7 participants dropped out, resulting in a sample size of 112. Assessments of cardiovascular risk and the OGTT could not be completed for 11 participants due to local COVID-19 lockdowns. Baseline characteristics for the whole group and per intervention arm are shown in Table 2. Study design As aforementioned, this research was part of the two-center PERSonalized glucose Optimization through Nutritional intervention (PERSON) study [12]. It involves two centers located in the Netherlands, Maastricht University Medical Center+ and Wageningen University & Research (WUR). The complete design and the CONSORT-diagram, which was approved by the local Medical Ethical Committee (NL63768.068.17), is published elsewhere [12]. The PERSON study was registered at a clinical trial register (ClinicalTrials.gov, NCT03708419) and executed according to the Declaration of Helsinki. Vascular measurements were performed at WUR (n=119) only, thus in a subgroup of the total PERSON study population. Before and during week 12 of the intervention, vascular function, cardiovascular risk factors, IR, and disposition index were assessed ( Figure 1 ), as described in more detail below. The focus of this manuscript is on vascular assessments, which were only performed in this subgroup of the PERSON study. Other results of the PERSON study have been recently published elsewhere [14]. Screening During screening, glucose and insulin values measured during a 7-point OGTT (time points 0, 15, 30, 45, 60, 90, 120) were used to calculate the muscle insulin sensitivity index (MISI) and hepatic insulin sensitivity index (HIRI). Calculations were based on Abdul-Ghani et al. [7]. The modelling of MISI was optimized by O’Donovan et al. [15]. HIRI and MISI have been validated against the golden standard hyperinsulinemic-euglycemic clamp [7,15]. The first blood sample (t=0) was drawn fasted from an intravenous cannula (antecubital vein). The remaining samples were taken after ingestion of a 200 ml 75 g glucose solution (Novolab). Data from The Maastricht study [16], from which a population with characteristics similar to the PERSON participants was selected, was used for MISI/HIRI tertile reference categories. Participants were classified as having MIR if their MISI was within the lowest tertile, and as LIR, if their HIRI was within the highest tertile [12]. Compared to The Maastricht Study, LIR prevalence was found to be lower in the first 163 participants of the PERSON study, wherefore the median HIRI of the PERSON study was used as cutoff thereafter. Education level, retirement status, and alcohol consumption habits were assessed during screening with questionnaires. A food frequency questionnaire (FFQ, validated, 163-items) assessed habitual dietary intake [17]. Diet intervention Participants were randomly allocated to follow either Phenotype diet (PhenoDiet) group A (LFHP for LIR, HMUFA for MIR), or PhenoDiet group B (LFHP for MIR, HMUFA for LIR), using center-specific minimization with randomization factors of 1.0 for the LIR/MIR phenotype, and 0.8 for age and sex, and a base probability of 0.7 by means of biased-coin [12]. During the 12-week intervention, participants had to remain weight stable, in order to assess the effect of the diet rather than weight loss. Participants were instructed to maintain their habitual physical activity levels. A more detailed description of the diet, instructions given to participants and exceptions can be found elsewhere [12]. Due to COVID-19 restrictions, some aspects of the intervention had to be adjusted, as the weekly visits were not possible anymore: on-site visits were substituted by phone/video calls and key products were delivered to participants at home. Vascular function CAR was assessed after an overnight fast (>10 h) with ultrasound (Terason uSmart 3300, Burlington, MA, USA) at baseline and during the last week of the intervention. CAR has been associated with coronary artery function, CVD risk, and disease progression in patients with peripheral arterial disease [20,21]. For CAR assessments, additional exclusion criteria applied: angina pectoris, Raynaud disease, chronic pain syndrome affecting the upper extremities, arteriovenous shunt, scleroderma, and heart infarct or heart failure within the last three months. Of the 119 participants, 105 were eligible for vascular function assessment, with 83 participants completing week-12 measurements (6 dropouts, 16 local COVID-19 lockdown). Three ultrasound recordings were excluded due to measurement problems, resulting in a total population for vascular function assessments of 80. The CAR test was performed after a minimum of 10 minutes supine rest. CAR measures the diameter change of the right common carotid artery in response to a 3-min cold pressor test (CPT) (sympathetic stimulus). During CPT, the left hand of the participant was immersed in cold water (≤4°C) up to the wrist. The average diameter of a 1-min baseline recording was compared to the maximum diameter response (in 10 second intervals) during the 3-min CPT, using wall-tracking and edge-detection software [22]. Data were filtered manually for major artefacts, caused for example by swallowing, breathing or probe movement. Analysis was done blinded and an independent assessor reviewed the analyses. In response to the CPT, the carotid artery can dilate or constrict. The direction of reactivity was determined by a positive (dilation) or negative (constriction) area under the curve (CAR AUC ). CAR% was then defined as the maximum dilation or constriction from baseline, divided by the baseline diameter. Cardiovascular disease risk factors CVD risk factors measured before the start of the intervention and during week 12 of the intervention include fasting levels of total cholesterol and high-density lipoprotein (Cobas Pentra C400 with ABX Pentra Cholesterol CP reagens or ABX Pentra HDL Direct, respectively). Blood pressure was measured in sitting position after 5 min rest (dominant arm, automated sphygmomanometer, average of two measurements). The Framingham risk score for cardiovascular disease was calculated as described by D’Agostino et al. [23], based on age, total cholesterol, HDL, treated/untreated systolic blood pressure, diabetes and smoking status. Two measurements of height, weight, and waist-/hip circumference were taken and averaged at each point of assessment Glucose homeostasis A 7-point OGTT was performed at baseline and repeated during week 12 of the dietary intervention. MISI and HIRI [7,15] were calculated as follows: MISI= (dGlucose/dt)/insulin [mean during OGTT in pmol/L], with dGlucose/dt being the rate of decay of plasma glucose concentration (mmol/L) during the OGTT. HIRI=glucose 0-30 [AUC in mmol/L*h] x insulin 0-30 [AUC in pmol/L*h]. Matsuda index [24] and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) [25] were calculated as follows: HOMA-IR: fasting glucose (mmol/L) × (fasting insulin (mU/L)/22.5). Matsuda index: 10,000 ÷ square root of (fasting plasma glucose (mmol/L) x fasting insulin (pmol/L) x (mean glucose T0, T30, T60, T90, T120 (mmol/L) x mean insulin T0, T30, T60, T90, T120 (pmol/L)). In case of one missing timepoint value (N=2), mean glucose/insulin were still calculated with the remaining timepoints. Disposition index was calculated as: Matsuda index * AUC30 insulin (pmol/L)/AUC30 glucose (mmol/L). AUC30 was calculated as the area under the curve from 0-30 minutes with the trapezoid method. Physical activity Physical activity was measured with the activPAL 3 micro (PAL Technologies Ltd., Glasgow, UK), starting during the baseline measurements and continuing for ~1 week during the first week of the dietary intervention. At the end of the intervention, physical activity was reassessed starting in week 11, continuing until the end of week 12 ( Figure 1 ). Only ‘free-living’ days ( Figure 1 , minimum of 1 weekend + 3 week days), where participants did not visit the university or had to fill in extensive questionnaires, were included in physical activity analysis. ActivPAL data were analyzed with an adapted script based on Winkler at al. [26]. Adaptations were made to include sleep/wake diaries filled in by participants. Statistical analysis Normally distributed data are presented as mean±SD, non-normal data as median [IQR]. Changes in the outcome variables for the total study population were assessed with a paired t-test or Wilcoxon signed-rank test in case of a non-normal distribution of the delta score (Week 12 - baseline). Analysis of intervention effects with repeated measures linear mixed models revealed a substantial violation of homoscedasticity for our primary outcome (vascular function). Therefore, differences in delta scores (week 12 - baseline) between interventions (PhenoDiet group A versus PhenoDiet group B; LFHP versus HMUFA) were analyzed with linear regression models, corrected for baseline values. In a second model we corrected additionally for age and sex. As participants lost weight during the intervention, which was not intended, we adjusted for weight change in model 3 ( Table S1-S2 ). In a forth model we corrected for changes in physical activity ( Table S1-S2 ). To this end, physical activity expressed as % of awake time, as it takes into account the interconnectedness between physical activity and sedentary behavior, meaning that a higher percentage of the day spent in physical activity results in a lower percentage spent in sedentary behavior. Analyses where done in R studio, R version 3.6.2 [27]. Results On average, participants (53.5% female) were 61±7 years old, with a median BMI of 27.6 [26.4;30.0] kg/m 2 . Participant characteristics of the total group (n=101) and per intervention arm can be found in Table 2 . Total physical activity, sedentary time, LIPA and MVPA time did not significantly change during the intervention ( Table 3 ). There was no significant difference between PhenoDiet group A versus group B or LFHP versus HMUFA diets for any of these outcomes ( Table 4-5 ). Glucose homeostasis Overall, dietary intervention increased the Matsuda index, while HOMA-IR decreased significantly and disposition index remained unchanged (Table 3) . The Matsuda-index (P=0.078) tended to increase more in PhenoDiet group B than A, while HOMA-IR and the disposition index were not significantly different between PhenoDiet groups ( Table 4 ). The LFHP diet resulted in a greater changes in Matsuda index and HOMA-IR compared to HMUFA ( Table 5 ), while changes in disposition index were not different between these diets (Tables 4-5) . Adjustment for weight change or physical activity change did not alter results for HOMA-IR, Matsuda index, or disposition index ( Tables S1-S2 ). Vascular function In the total study population, vascular function did not change during the intervention ( Table 3 ). ∆CAR% and ∆CAR AUC were not different between PhenoDiet group A and group B ( Table 4 ), and we found no significant difference between both diets (LFHP/HMUFA) on CAR ( Table 5 ). Adjustment for weight change or changes in physical activity did not change these results ( Tables S1-S2 ). Cardiovascular risk factors Overall, dietary intervention decreased CVD risk factors(SBP, total cholesterol, HDL, total cholesterol/HDL ratio, Table 3 ). The Framingham risk score also decreased significantly ( Table 3 ). Despite the goal of keeping participants weight stable, body weight decreased by 2.1±2.3 kg (P<0.001), while waist-to-hip ratio did not change significantly (P=0.159). Total cholesterol (P=0.052) tended to decrease more in PhenoDiet group B than A, but changes in other CVD risk factors in were not different between PhenoDiet group A and B ( Table 4 ). LFHP resulted in a greater decrease in total cholesterol, HDL, and DBP compared to MUFA ( Table 5 ). After adjustment for weight change or physical activity change, the change in DBP was not significantly different between LFHP and HMUFA (P=0.05, P=0.079, respectively), whilst correction for weight or physical activity did not alter the other outcomes ( Tables S1-S2 ). The decrease in SBP, Framingham risk score, and weight was not different between LFHP versus HMUFA ( Table 5 ). Discussion As there is a close link between IR and vascular dysfunction [2,13], the primary objective of the present study was to determine whether a more personalized diet, designed to optimize glucose homeostasis, has more pronounced beneficial effects on vascular function and cardiovascular risk factors. We found that consumption of a healthy diet for 12 weeks improved cardiovascular risk factors and IR, but did not significantly affect vascular function and disposition index, in individuals with either MIR or LIR. Moreover, personalized diets according to IR phenotype showed that the Matsuda index and total cholesterol concentrations tended to improve more in PhenoDiet group B versus A, while no significant superiority was found for other outcomes related to vascular function or cardiovascular risk. Interestingly, however, the LFHP diet resulted in greater improvements in several cardiovascular risk factors and measures of IR, compared to HMUFA. In the total study population, the 12-week diet intervention improved cardiovascular risk factors and insulin resistance. The Framingham risk score, which is linked to 10-year risk of CVD [23], also decreased. Both diets represent healthy diets, in line with dietary guidelines [28], with improvements in cardiovascular and metabolic health being in line with previous findings [6,29–31]. In the present study, pre-intervention habitual dietary intake of saturated fat (14% of energy intake) and fiber intake (2.5 g/MJ) did not meet the recommendations (saturated fat <10% of energy intake, 3.4 g/MJ fiber). The targeted dietary intake of either diet (HMUFA or LFHP) improved dietary intake of the participants. In contrast to our expectations, vascular function did not improve, despite significant improvements in insulin resistance and cardiovascular risk factors during the intervention. CAR is associated with cardiovascular risk factors [21], and vascular function is closely linked to IR [2]. However, this link between IR and vascular function is largely investigated in the peripheral vasculature, where insulin stimulates the production of NO, resulting in NO-dependent vasodilation and increased uptake of glucose [2]. Flow-mediated dilation (FMD), a measure of peripheral vascular function, has been shown to improve with diet interventions within 2 months [32,33]. FMD may be more responsive than the carotid reactivity to a diet as both measures reflect a different part of the vascular system (peripheral versus central) or are mediated through distinct pathways (shear stress versus sympathetic nervous system) [34,35]. These differences between both measures of vascular functio may explain why CAR did not significantly change, despite slight but significant improvements in IR. Alternatively, a longer diet intervention may be required for improvements in cardiovascular risk factors and insulin resistance to translate to improvements in CAR. Future studies could benefit from incorporating more direct measures of insulin-mediated vascular function or FMD, together with measures of central vascular function. In contrast to our expectations, linking the tissue-specific IR phenotype (i.e. predominantly liver or muscle IR) to a distinct diet (LFHP, HMUFA) did not result in superior improvements in cardiovascular risk factors or vascular function, except for a trend for improved insulin resistance (Matsuda index) and total cholesterol concentrations in PhenoDiet group B versus A. Furthermore, we did not detect distinct effects between both PhenoDiet groups on the disposition index, which was the primary outcome of the PERSON study [12]. Recently, we published the findings pertaining to the main outcomes of the PERSON study, which included 242 participants, and found significantly greater improvements in glucose homeostasis and insulin sensitivity in PhenoDiet group B versus A [14]. Since we included a subset of the full dataset, this study was not powered to detect changes in glucose homeostasis between the PhenoDiet groups. While a healthy diet improved cardiovascular risk factors in the total study population, we found a more pronounced decrease in total cholesterol and DBP in individuals who followed the LFHP diet. Moreover, in contrast to our expectations, both diets reduced HDL concentration. Previous studies show conflicting results regarding the effects of diet on HDL, with increased or unchanged HDL levels being reported after a Mediterranean diet [29,30] and decreased to increased HDL levels after a low-fat diet [29,30,36–38]. The restriction of alcohol consumption during the dietary intervention to ≤1 glass/day might have contributed to reductions in HDL, as alcohol consumption can increase HDL levels [39]. However, the decrease in HDL was not significantly different between participants with low (≤3.5 glasses/week) versus high (≥3.5 glasses/week) baseline alcohol consumption (split at median, P=0.986, data not shown). Despite a reduction in HDL, the total cholesterol/HDL ratio decreased significantly, suggesting a relatively larger decline in total cholesterol than the change in HDL, with no difference between the diets. These changes in blood lipids may have a beneficial impact on CVD risk, as lower total cholesterol/HDL ratio is associated with lower risk for CVD [40,41]. A greater improvement in DBP has previously been reported with a LFHP diet, compared to a Mediterranean diet, in individuals with type 2 diabetes [29]. A bigger (nonsignificant) weight reduction (-2.5 kg [-3.6;-0.5] versus -1.6 kg [-3.6;0.1]), and (non-significant) increase in physical activity (15.8 min [-44.3;43.8] versus -7.7 min [-50.4;26.0]) with the LFHP diet may, at least partially, contribute to the differences in ∆DBP observed between the LFHP and HMUFA diets. Adjustment for changes in weight or changes in physical activity resulted in non-significant differences between LFHP and HMUFA for ∆DBP, although a trend for difference (P<0.1) remained present. Even small increases in physical activity (even at light intensity), by breaking up prolonged periods of sitting, have previously been shown to be beneficial for blood pressure [42]. The association between weight and blood pressure is moreover well established [43]. A strength of this study is the objective assessment of physical activity before and after the intervention. In the total study population, physical activity did not change significantly, and adjustment for changes in physical activity did not alter our results substantially. This suggests that our results are indeed related to the diet intervention rather than (subconscious) changes in physical activity. A few limitations of this study need to be acknowledged. This study was designed to keep participants weight stable, to be able to attribute outcomes to diet composition rather than weight loss. Despite our efforts to keep participants weight stable, by adjusting energy groups when participants lost weight, participants lost an average of 2 kg during the 12-week intervention. However, results were largely unaltered after adjustment for weight change. Another limitation of our study relates to its power. Some of our outcomes (CVD risk, insulin resistance) represent a subgroup analysis of the PERSON study and may therefore be underpowered [14]. However, in previous work, we found that effects of a lifestyle intervention on CAR have previously been seen with a much smaller study population (N=19) [44]. In conclusion, in individuals with LIR or MIR, a healthy diet (LFHP or HMUFA) can improve cardiovascular risk factors and insulin resistance within 12 weeks, while we found no adaptations in the common carotid artery vascular function. Importantly, assigning individuals based on their IR phenotype (LIR or MIR) to a distinct diet tended to further improve insulin sensitivity (Matsuda index) and total cholesterol concentrations, but did not alter other cardiovascular risk factors or vascular function. In addition, comparing both types of diet, we found that LFHP resulted in greater improvements in some markers of CVD risk and IR. Taken together, these results highlight the benefits of diet to improve cardiovascular risk and IR, with our data suggesting that the diet type per se (LFHP) has larger effects compared to personalization of the diet based on the IR phenotype. Declarations CLINICAL TRIAL REGISTRATION: The study design was approved by the local Medical Ethical Committee (NL63768.068.17). The PERSON study was registered at a clinical trial register (ClinicalTrials.gov, NCT03708419) and executed according to the Declaration of Helsinki. All study participants provided written consent before participation. FUNDING : The project is organized by and executed under the auspices of TiFN, a public -private partnership on precompetitive research in food and nutrition. The authors have declared that no competing interests exist in the writing of this publication. Funding for this research was obtained from the industry partners DSM Nutritional Products, FrieslandCampina, and Danone Nutricia Research; and the Netherlands Organisation for Scientific Research. DISCLOSURE : Authors report no competing interests. AUTHOR CONTRIBUTIONS : LW participated in data collection, performed the data analysis, and drafted the manuscript. AG was responsible for execution of the study. GBH was responsible for data management. EEB was project leader and obtained funding for the project. DHJT also obtained funding for the project. EEB, LAA, GHG, EJMF, DHJT co-designed the study. LAA, MTEH, GHG, and DHJT supervised the research activities. All authors participated in the discussion of results and revision of the article. AAll authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.. Data availability Aggregated and individual participant data and associated supporting documents will be made available from the corresponding author upon reasonable request. Individual participant data that underlie the results reported in this article, after deidentification, will be shared upon reasonable request after publication and ending 36 months following provision of the data to researchers who provide a methologically sound proposal. Proposals should be directed to the corresponding author, Dick Thijssen. All remaining data can be found in the Article and Supplementary. References Blaak EE, Antoine J-M, Benton D, Björck I, Bozzetto L, Brouns F et al (2012) Impact of postprandial glycaemia on health and prevention of disease. 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Participant characteristics Total N=101 LFHP-PhenoDiet A N=17 LFHP- PhenoDiet B N=31 HMUFA- PhenoDiet A N=36 HMUFA- PhenoDiet B N=17 P-value Age, years 61±7 61±4 62±8 60±8 60±8 0.671 Sex, female 54 (53.5%) 9 (52.9%) 18 (58.1%) 19 (52.8%) 8 (47.1%) 0.907 BMI, kg/m 2 27.6 [26.4; 30.0] 26.7 [26.2; 27.6] 27.4 [26.3; 29.9] 27.6 [26.4; 29.7] 29.6 [28.4; 31.4] 0.061 Statins, yes 5 (5.0%) 1 (5.9%) 3 (9.7%) 1 (2.8%) 0 (0.0%) 0.569 Antihypertensives, yes 9 (8.9%) 1 (5.9%) 4 (12.9%) 3 (8.3%) 1 (5.9%) 0.892 Retired, yes 32 (31.7%) 3 (17.6%) 13 (41.9%) 11 (30.6%) 5 (29.4%) 0.374 Education level a Low 4 (4.0%) 2 (11.8%) 2 (6.5%) 0 (0.0%) 0 (0.0%) 0.327 Int 39 (39.0%) 8 (47.1%) 12 (38.7%) 14 (40.0%) 5 (29.4%) High 57 (57.0%) 7 (41.2%) 17 (54.8%) 21 (60.0%) 12 (70.6%) Total energy, kcal 2178.0±589.5 1994.1 [1749.0; 2368.1] 1955.5 [1839.2; 2229.0] 2251.3 [1721.8; 2524.4] 2491.4 [1858.8; 3114.2] 0.257 Carbohydrates, energy% 42.3 [39.4; 45.7] 41.2 [37.8; 47.3] 42.1 [40.6; 45.7] 42.6 [37.1; 45.9] 42.5 [40.0; 43.3] 0.690 Protein, energy% 15.4 [14.4; 16.6] 15.6±1.8 15.9±2.0 15.6±2.5 15.2±1.9 0.801 Fat, energy% 36.5 [34.1; 40.5] 35.4 [33.0; 43.9] 36.5 [34.4; 38.2] 36.5 [33.2; 40.2] 36.4 [34.9; 42.2] 0.868 Saturated fat, energy % 13.9 [12.0; 15.3] 13.6 [11.3; 15.1] 13.9 [11.9; 15.7] 14.2 [12.3; 15.2] 13.9 [12.3; 16.6] 0.927 Fiber, g/MJ 2.5±0.6 2.5 [2.1; 2.9] 2.5 [2.1; 2.9] 2.5 [2.0; 3.1] 2.4 [2.0; 2.9] 0.946 Alcohol, glasses/week 3.5 [0.9; 6.0] 4.0 [0.0; 9.0] 3.0 [0.5; 6.0] 3.0 [2.0; 5.0] 4.0 [0.5; 6.0] 0.960 LFHP, low-fat high-protein diet; HMUFA, high-monounsaturated fatty acid diet; BMI, body mass index. a Low: no education, primary education, lower/preparatory vocational education, lower general secondary education, medium: int, intermediate vocational education, higher general senior secondary education, pre-university secondary education, high: higher vocational education, university. For education level and glasses alcohol: total N=100. Normally distributed data are presented as mean±SD, non-normal data as median [IQR]. Table 3. Outcomes at baseline and week 12: total study population Outcome Total group N=80 P-value CAR%, % N=80 Baseline 2.39 [1.37; 3.34] 0.485 Week 12 2.23 [1.25; 3.00] Delta -0.18 [-1.40; 1.08] CAR AUC , cms N=80 Baseline 1.57 [0.55; 2.33] 0.783 Week 12 1.42 [0.52; 1.99] Delta -0.15 [-0.90; 0.88] SBP, mmHg N=100 Baseline 124 [114; 136] <0.001 Week 12 118 [110; 128] Delta -5±10 DBP, mmHg N=100 Baseline 73±10 0.004 Week 12 71±10.0 Delta -2±7 TC, mmol/L N=97 Baseline 5.45±1.04 <0.001 Week 12 4.83±0.93 Delta -0.62 [-0.89; -0.36] HDL-C, mmol/L N=97 Baseline 1.30 [1.11; 1.48] <0.001 Week 12 1.22 [1.06; 1.36] Delta -0.09±0.11 TC/HDL-C, ratio N=97 Baseline 4.31±1.09 <0.001 Week 12 4.08±1.02 Delta -0.23±0.38 FRS N=96 Baseline 11.6±3.8 <0.001 Week 12 10.4±3.8 Delta -1.0 [-2.0; 0.0] Weight, kg N=101 Baseline 85.6±10.8 <0.001 Week 12 83.5±10.8 Delta -2.1±2.3 WHR N=101 Baseline 0.960 [0.910; 1.010] 0.159 Week 12 0.950 [0.890; 1.010] Delta -0.006±0.042 Matsuda N=101 Baseline 13.1 [9.7; 17.4] <0.001 Week 12 14.0 [11.1; 20.1] Delta 1.3 [-0.8; 4.4] HOMA-IR N=101 Baseline 1.6 [1.3; 2.1] 0.001 Week 12 1.4 [1.1; 2.0] Delta -0.1 [-0.4; 0.1] Disposition index N=101 Baseline 420.1 [293.8; 647.6] 0.362 Week 12 438.2 [298.4; 627.3] Delta 16.1±177.0 Sitting, h N=93 Baseline 9.5±1.4 0.586 Week 12 9.5±1.5 Delta 0.1±1.3 PA, h N=93 Baseline 6.2±1.6 0.315 Week 12 6.1±1.7 Delta -0.1±1.2 LIPA, h N=93 Baseline 5.0±1.3 0.111 Week 12 4.9±1.4 Delta -0.2±1.0 MVPA, h N=93 Baseline 1.2 [0.9; 1.4] 0.338 Week 12 1.2 [0.8; 1.5] Delta 0.0 [-0.2; 0.3] CAR, carotid artery reactivity; AUC, area under the curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL, low-density lipoprotein; FRS, Framingham risk score; WHR, waist-to-hip ratio; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; PA, physical activity; LIPA, light-intensity PA; MVPA, moderate-to-vigorous PA. Normally distributed data are presented as mean±SD, non-normal data as median [IQR]. P-value for differences between baseline and week 12. Table 4. Changes in vascular function, cardiovascular risk factors, glucose homeostasis and physical activity: PhenoDiet group A (N=53) versus PhenoDiet group B (N=48). Outcome Diet group Baseline Week 12 ∆ Model 1* Model 2* β [95% CI] P-value β [95% CI] P-value CAR%, % N=80 B 2.48 [1.35; 3.91] 2.24 [1.32; 2.74] -0.27 [-1.43; 0.73] REF - REF - A 2.33 [1.38; 2.96] 2.07 [1.21; 3.16] -0.17 [-1.29; 1.31] 0.03 [-0.84–0.89] 0.952 -0.06 [-0.92–0.81] 0.899 CAR AUC , cm*s N=80 B 1.67 [0.48; 2.91] 1.79 [0.53; 2.20] 0.19 [-0.78; 1.10] REF - REF - A 1.54 [0.79; 2.17] 1.05 [0.53; 1.86] -0.34 [-0.98; 0.80] -0.30 [-0.98–0.38] 0.376 -0.37 [-1.04–0.31] 0.287 SBP, mmHg N=100 B 128±16 120±12 -8±11 REF - REF - A 122±16 119±15 -3±10 2.84 [-0.60–6.28] 0.105 2.76 [-0.7–6.23] 0.117 DBP, mmHg N=100 B 74±10 71±9 -3±7 REF - REF - A 73±11 71±11 -1±6 1.32 [-1.13–3.76] 0.287 1.06 [-1.38–3.50] 0.392 TC, mmol/L N=97 B 5.44±1.06 4.72±0.92 -0.66 [-0.95; -0.39] REF - REF - A 5.47±1.03 4.92±0.94 -0.59 [-0.85; -0.27] 0.18 [-0.00–0.37] 0.052 0.18 [-0.01–0.37] 0.059 HDL-C, mmol/L N=97 B 1.30 [1.10; 1.47] 1.18 [0.99; 1.33] -0.10±0.12 REF - REF - A 1.30 [1.16; 1.49] 1.23 [1.07; 1.38] -0.07±0.11 0.04 [-0.01–0.08] 0.103 0.04 [-0.01–0.08] 0.094 TC/HD-C, ratio N=97 B 4.34±1.10 4.10±1.07 -0.25±0.40 A 4.28±1.09 4.06±0.97 -0.21±0.36 0.03 [-0.12–0.17] 0.723 0.02 [-0.12–0.16] 0.813 FRS N=96 B 11.8±4.0 10.3±3.8 -1.5 [-3.0; 0.0] REF - REF - A 11.3±3.7 10.5±3.9 -1.0 [-1.0; 0.0] 0.60 [-0.17–1.37] 0.124 0.48 [-0.22–1.18] 0.179 Weight, kg N=101 B 86.6±9.2 84.1±9.0 -2.5±2.2 REF - REF - A 84.7±12.1 83.0±12.3 -1.7±2.4 0.74 [-0.17–1.66] 0.110 0.63 [-0.28–1.55] 0.173 WHR N=101 B 0.965 [0.910; 1.010] 0.945 [0.898; 1.002] -0.004±0.046 REF - REF - A 0.960 [0.910; 1.000] 0.950 [0.880; 1.010] -0.007±0.038 -0.00 [-0.02–0.01] 0.563 -0.01 [-0.02–0.01] 0.336 Matsuda N=101 B 12.2 [9.2; 15.2] 14.8 [11.2; 20.7] 2.1 [0.0; 4.8] REF - REF - A 13.4 [10.1; 18.3] 13.5 [11.1; 19.8] 0.4 [-1.3; 4.1] -1.69 [-3.58–0.20] 0.078 -1.58 [-3.48–0.31] 0.101 HOMA-IR N=101 B 1.7 [1.4; 2.2] 1.4 [1.1; 2.0] -0.2 [-0.6; 0.1] REF - REF - A 1.5 [1.1; 2.0] 1.6 [1.1; 1.9] -0.1 [-0.4; 0.2] 0.09 [-0.15–0.32] 0.465 0.07 [-0.16–0.30] 0.560 Disposition index N=101 B 416.4 [253.5; 566.1] 432.1 [280.9; 589.0] 22.2±169.7 REF - REF - A 431.7 [320.3; 685.2] 443.1 [299.8; 655.1] 10.6±184.8 1.46 [-64.68–67.61] 0.965 4.21 [-62.81–71.22] 0.901 Sitting, h N=93 B 9.3±1.4 9.3±1.5 0.0 [-0.7; 0.9] REF - REF - A 9.6±1.4 9.7±1.6 -0.1 [-0.9; 0.9] 0.19 [-0.30–0.68] 0.442 0.18 [-0.31–0.67] 0.467 PA, h N=93 B 6.3±1.6 6.4±1.7 0.2 [-0.8; 0.7] REF - REF - A 6.1±1.6 5.9±1.6 -0.1 [-0.8; 0.5] -0.31 [-0.79–0.18] 0.213 -0.29 [-0.77–0.20] 0.241 LIPA, h N=93 B 5.1 [4.3; 6.0] 4.9 [4.1; 5.8] -0.0 [-0.6; 0.5] REF - REF - A 4.9 [4.1; 5.6] 4.8 [3.9; 5.7] -0.2 [-0.6; 0.3] -0.25 [-0.66–0.15] 0.220 -0.24 [-0.64–0.17] 0.252 MVPA, h N=93 B 1.2 [0.9; 1.5] 1.2 [1.0; 1.6] -0.0 [-0.2; 0.2] REF - REF - A 1.2 [0.9; 1.4] 1.1 [0.8; 1.5] -0.0 [-0.2; 0.3] -0.05 [-0.20–0.10] 0.491 -0.05 [-0.20–0.10] 0.523 CAR, carotid artery reactivity; AUC, area under the curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL, high-density lipoprotein; FRS, Framingham risk score; WHR, waist-to-hip ratio; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; PA, total physical activity, LIPA, light-intensity physical activity; MVPA, moderate-to vigorous physical activity. Normally distributed data are presented as mean±SD, non-normal data as median [IQR]. *Linear regression models testing differences in the change (week 12 minus baseline) in the outcome variable between PhenoDiet group A and group B. Model 1: corrected for baseline values; Model 2: corrected for baseline values, age, sex. Table 5. Changes in vascular function, cardiovascular risk factors, glucose homeostasis and physical activity: LFHP (N=48) versus HMUFA (N=53) diet. Outcome Diet Baseline Week 12 ∆ Model 1* Model 2* β [95% CI] P-value β [95% CI] P-value CAR%, % N=80 LFHP 2.40 [1.30; 3.79] 1.89 [1.25; 2.59] -0.28 [-1.45; 1.03] REF - REF - HMUFA 2.36 [1.40; 3.00] 2.52 [1.32; 3.34] 0.10 [-1.13; 1.20] 0.40 [-0.46–1.26] 0.362 0.36 [-0.51–1.23] 0.410 CAR AUC , cm*s N=80 LFHP 1.48 [0.48; 2.90] 1.00 [0.36; 1.89] 0.08 [-0.81; 0.56] REF - REF - HMUFA 1.68 [0.78; 2.25] 1.68 [0.67; 2.06] -0.20 [-0.91; 0.95] 0.34 [-0.34–1.02] 0.324 0.35 [-0.34–1.03] 0.315 SBP, mmHg N=100 LFHP 123 [111; 134] 117 [108; 124] -6±11 REF - REF - HMUFA 125 [117; 136] 121 [114; 132] -4±10 3.28 [-0.12–6.67] 0.058 3.30 [-0.15– 6.75] 0.061 DBP, mmHg N=100 LFHP 72±9 69±9 -3±7 REF - REF - HMUFA 74±12 73±10 -1±7 2.73 [0.34–5.13] 0.026 2.48 [0.07–4.89] 0.044 TC, mmol/L N=97 LFHP 5.49±1.04 4.73±0.94 -0.68 [-1.00; -0.44] REF - REF - HMUFA 5.42±1.04 4.91±0.93 -0.57 [-0.82; -0.20] 0.24 [0.06–0.42] 0.011 0.23 [0.05–0.42] 0.014 HDL-C, mmol/L N=97 LFHP 1.33 [1.13; 1.48] 1.23 [1.04; 1.35] -0.11±0.10 REF - REF - HMUFA 1.30 [1.08; 1.48] 1.22 [1.07; 1.37] -0.06±0.12 0.05 [0.01–0.09] 0.018 0.05 [0.01–0.10] 0.013 TC/HDL-C, ratio N=97 LFHP 4.32±1.12 4.09±1.11 -0.23±0.42 REF - REF - HMUFA 4.30±1.08 4.07±0.93 -0.23±0.34 -0.00 [-0.15–0.14] 0.968 -0.02 [-0.16–0.12] 0.800 FRS N=96 LFHP 11.5±3.7 10.1±3.5 -1.0 [-2.0; 0.0] REF - REF - HMUFA 11.7±4.0 10.6±4.1 -0.5 [-2.0; 0.0] 0.32 [-0.46–1.09] 0.420 0.32 [-0.39–1.03] 0.374 Weight, kg N=101 LFHP 83.6±10.3 81.3±10.0 -2.5 [-3.6; -0.5] REF - REF - HMUFA 87.4±11.1 85.5±11.3 -1.6 [-3.6; 0.1] 0.41 [-0.52–1.35] 0.383 0.37 [-0.56–1.30] 0.429 WHR N=101 LFHP 0.950 [0.910; 1.010] 0.935 [0.880; 1.002] -0.007±0.045 REF - REF - HMUFA 0.970 [0.910; 1.010] 0.960 [0.910; 1.010] -0.005±0.039 0.00 [-0.01–0.02] 0.743 0.00 [-0.01–0.02] 0.883 Matsuda N=101 LFHP 13.3 [10.1; 17.9] 17.0 [11.9; 22.6] 2.1 [0.5; 6.5] REF - REF - HMUFA 13.0 [9.7; 16.2] 12.7 [10.5; 17.5] 0.2 [-1.3; 3.3] -2.73 [-4.56–(-0.90)] 0.004 -2.60 [-4.45–(-0.75)] 0.006 HOMA-IR N=101 LFHP 1.5 [1.2; 2.1] 1.2 [0.9; 1.7] -0.3 [-0.5; 0.1] REF - REF - HMUFA 1.8 [1.3; 2.3] 1.7 [1.3; 2.0] -0.0 [-0.4; 0.1] 0.26 [0.04–0.49] 0.021 0.24 [0.01–0.47] 0.038 # Disposition index N=101 LFHP 433.4 [257.2; 732.1] 436.0 [284.6; 654.5] 26.0 [-80.4; 94.9] REF - REF - HMUFA 397.0 [309.5; 574.7] 443.1 [299.8; 597.3] 23.0 [-85.5; 105.2] -2.71 [-68.78–63.36] 0.935 0.89 [-66.31–68.09] 0.979 Sitting, h N=93 LFHP 9.4±1.4 9.2±1.5 -0.0 [-1.0; 0.6] REF - REF - HMUFA 9.6±1.4 9.8±1.5 -0.0 [-0.6; 1.0] 0.44 [-0.04–0.92] 0.071 0.42 [-0.06–0.90] 0.088 PA, h N=93 LFHP 6.2±1.6 6.3±1.5 0.3 [-0.7; 0.7] REF - REF - HMUFA 6.2±1.5 5.9±1.7 -0.1 [-0.8; 0.4] -0.35 [-0.82–0.13] 0.155 -0.31 [-0.80–0.17] 0.204 LIPA, h N=93 LFHP 5.0±1.4 5.0±1.3 0.1 [-0.6; 0.5] REF - REF - HMUFA 5.0±1.3 4.7±1.4 -0.2 [-0.6; 0.3] -0.30 [-0.70–0.10] 0.145 -0.27 [-0.68–0.14] 0.196 MVPA, h N=93 LFHP 1.2 [0.9; 1.4] 1.2 [0.9; 1.6] 0.1±0.3 REF - REF - HMUFA 1.2 [0.9; 1.5] 1.1 [0.8; 1.5] 0.0±0.4 -0.06 [-0.21–0.09] 0.457 -0.05 [-0.21–0.10] 0.498 LFHP, low-fat, high-protein diet; HMUFA, high-monounsaturated fatty acid diet; CAR, carotid artery reactivity; AUC, area under the curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL, high-density lipoprotein; FRS, Framingham risk score; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; PA, total physical activity, LIPA, light-intensity physical activity; MVPA, moderate-to vigorous physical activity. Normally distributed data are presented as mean±SD, non-normal data as median [IQR]. *Linear regression models testing differences in the change (week 12 minus baseline) in the outcome variable between LFHP and HMUFA diets. Model 1: corrected for baseline values; Model 2: corrected for baseline values, age, sex. # For HOMA-IR, significance was driven by two participants. Exclusion resulted in non-significant differences (P=0.158). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementalinformation.docx Supplemental tables and figures Studyprotocolfinalversion00624June2019.pdf Study Protocol Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4162501","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":294573331,"identity":"42acbf8d-e5ab-47a6-93e9-a0ff5dbf249f","order_by":0,"name":"Dick Thijssen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIiWNgGAWjYBACe2YGZgbGfwd4GBtA3AogvgHEPGBJ5gZsWgybgVoYDGBazqBoYcSqxeAARAuEx9hGjJbjzI8NGAzuyDD3H3/4uHDePXm+283HJN62bZNnkG7EruUwm3ECg8EzHsYZOcbGM7cVG868cyxNcm7bbcMGmYM4tPAwHwCRjDN42KR5tyUwbriRYybN23Y7gUEikYCW/uPPf/POSbAnSksCWEtDghkzb0NCIkEths1sxgYJYIflGEvzHEtIBvol2XLOuduGbTi02PMffizxweCwvSEwxD7z1CTY9t1uPnjjTdlteX6J5APYtIBBAsg6DBPZcKqHAnlCCkbBKBgFo2DkAgCPrGScgVIdLAAAAABJRU5ErkJggg==","orcid":"","institution":"Radboud University Nijmegen Medical Centre","correspondingAuthor":true,"prefix":"","firstName":"Dick","middleName":"","lastName":"Thijssen","suffix":""},{"id":294573332,"identity":"a4e62685-33b5-471b-b6bf-15ced00227c0","order_by":1,"name":"Lisa Wanders","email":"","orcid":"","institution":"Radboud University Nijmegen Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Wanders","suffix":""},{"id":294573333,"identity":"99f2f8e5-bca1-42eb-b908-2988d0b34371","order_by":2,"name":"Anouk Gijbels","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Anouk","middleName":"","lastName":"Gijbels","suffix":""},{"id":294573334,"identity":"41ae3394-d2cf-40bc-a8f8-2bc44ebdf187","order_by":3,"name":"Gaby Hul","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Gaby","middleName":"","lastName":"Hul","suffix":""},{"id":294573335,"identity":"964479e9-4e21-417b-a10e-a5bdbf76d594","order_by":4,"name":"Edith Feskens","email":"","orcid":"","institution":"Wageningen University Research","correspondingAuthor":false,"prefix":"","firstName":"Edith","middleName":"","lastName":"Feskens","suffix":""},{"id":294573336,"identity":"20d05a2d-6e0b-4188-9043-2e2415e312f7","order_by":5,"name":"Lydia Afman","email":"","orcid":"","institution":"Wageningen University Research","correspondingAuthor":false,"prefix":"","firstName":"Lydia","middleName":"","lastName":"Afman","suffix":""},{"id":294573337,"identity":"b50aea45-3be7-46b8-b16c-f8f9efb936ae","order_by":6,"name":"Ellen Blaak","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Ellen","middleName":"","lastName":"Blaak","suffix":""},{"id":294573338,"identity":"4fdb4d4d-a44b-4aea-b5ba-2b5b3d0f5a03","order_by":7,"name":"Maria Hopman","email":"","orcid":"","institution":"Radboud University Nijmegen Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Hopman","suffix":""},{"id":294573339,"identity":"fcd82db4-c258-43d3-8a2d-ab43fd4a295d","order_by":8,"name":"Gijs Goossens","email":"","orcid":"","institution":"Maastricht University","correspondingAuthor":false,"prefix":"","firstName":"Gijs","middleName":"","lastName":"Goossens","suffix":""}],"badges":[],"createdAt":"2024-03-25 10:15:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4162501/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4162501/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55341498,"identity":"de04c1e4-fa54-4f0a-9aa9-62b7772f048f","added_by":"auto","created_at":"2024-04-26 02:15:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":204232,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design. LIR, liver insulin resistance; MIR, muscle insulin resistance; LFHP, low-fat, high-protein diet; HMUFA, high-monounsaturated fatty acid diet; B, Baseline; CAR, carotid artery reactivity; CVRF, cardiovascular risk factors; OGTT, oral glucose tolerance test. Line-pattern background indicates PhenoDiet group A, no pattern PhenoDiet group B.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-4162501/v1/f3a69db5fcb55fac28f8a3c0.png"},{"id":59277653,"identity":"3fa191b8-f84e-4698-8261-15a9aabb0dde","added_by":"auto","created_at":"2024-06-28 14:38:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271667,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4162501/v1/02d324b9-3230-4573-9f1e-2e95d0840b99.pdf"},{"id":55340806,"identity":"b874e87d-dda7-494e-af8e-4cbbf51c0cee","added_by":"auto","created_at":"2024-04-26 02:07:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25769,"visible":true,"origin":"","legend":"Supplemental tables and figures","description":"","filename":"Supplementalinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4162501/v1/be7c0c53b9003bee8566156d.docx"},{"id":55340812,"identity":"0c544465-b03f-4292-bcf9-a598d865c912","added_by":"auto","created_at":"2024-04-26 02:07:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1657382,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Protocol\u003c/p\u003e","description":"","filename":"Studyprotocolfinalversion00624June2019.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4162501/v1/dfdb810a9c10df0a89adf4f3.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Impact of a 12-week personalized dietary intervention on vascular function and cardiovascular risk factors","fulltext":[{"header":"What is already known?","content":"\u003cul\u003e\n \u003cli\u003eDietary interventions have shown promising results for improving insulin resistance and vascular function\u003c/li\u003e\n \u003cli\u003eRecent literature suggests that the success of a diet might be related to the metabolic phenotype of an individual; which can be predominantly in the muscle (MIR) or liver (LIR).\u003c/li\u003e\n \u003cli\u003eWhether\u0026nbsp;optimizing the diet to improve glucose homeostasis may translate into superior effects on cardiovascular risk factors is currently unknown.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat are the new findings?\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eA healthy diet for 12 weeks in insulin resistant individuals with obesity or overweight improved cardiovascular risk factors and insulin resistance.\u003c/li\u003e\n \u003cli\u003ePersonalized diets according to insulin resistance phenotype showed that the Matsuda index and total cholesterol tended to improve more, while no superiority was found for other outcomes.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eA low fat-high protein diet resulted in greater improvements in cardiovascular risk factors and measures of IR, compared to\u0026nbsp;high-monounsaturated fatty acid diet.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eHow do the results change clinical practice?\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOur results highlight the benefits of a healthy diet to improve cardiovascular risk and insulin resistance in individuals with insulin resistance who are obese or overweight\u003c/li\u003e\n \u003cli\u003eOur study suggests that the diet type \u003cem\u003eper se\u003c/em\u003e, which superiority of a low fat-high protein diet over high-monounsaturated fatty acid diet, to have larger effects compared to personalization of the diet based on the IR phenotype.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eThe rising prevalence of obesity in the Western world is a major risk factor for insulin resistance (IR), and ultimately type 2 diabetes (T2D) and cardiovascular disease (CVD)\u0026nbsp;[1]. Besides its metabolic role in glucose metabolism, insulin also acts on the vasculature\u0026nbsp;[2]. Especially in the postprandial state, IR is characterized by hyperglycemia, hyperinsulinemia, lipotoxicity, oxidative stress, and inflammation. These metabolic aberrations are also associated with the inhibition of insulin-stimulated production of NO, increased ET-1 release and increased expression of vascular adhesion molecules in the endothelium, contributing to endothelial dysfunction and development of atherosclerosis\u0026nbsp;[2]. Increased production of reactive oxygen/nitrogen species further reduces the availability of NO. Consequently, impairments in vascular function can worsen perturbations in glucose homeostasis by reducing blood flow and delivery of insulin and glucose to the tissues\u0026nbsp;[2].\u003c/p\u003e\n\u003cp\u003eDietary interventions have shown promising results for improving IR\u0026nbsp;[3]. Due to the close relationship between IR and vascular dysfunction, it has been suggested that interventions that improve IR will also improve vascular function, and \u003cem\u003evice versa\u003c/em\u003e [4]. For instance, the Mediterranean diet seems beneficial for both, IR and vascular function [5]. Interestingly, recent literature suggests that the success of a diet in improving glucose homeostasis might be related to the metabolic phenotype of an individual [6]. While IR can affect the liver and skeletal muscle simultaneously, it can also be localized predominantly in the muscle (MIR) or liver (LIR) [6,7]. Studies show that the LIR- or MIR-phenotypes are associated with distinct lipidome [8], metabolome [9], and adipose tissue inflammatory transcriptome and systemic inflammatory profiles [10]. Similarly, the IR-phenotype may determine its effect to dietary interventions [6]. Long-term adherence to a Mediterranean diet has previously been shown to be more beneficial for those with MIR, and a low-fat, high complex carbohydrate diet for those with LIR, with respect to glucose homeostasis [6]. Similarly, high protein diets have been shown to reduce liver fat and inflammation in individuals with type 2 diabetes mellitus [11]. Thus, a low-fat, high-protein, high-fiber diet (LFHP) may be an optimal diet for individuals with LIR, while a Mediterranean-type diet (high in monounsaturated fat, HMUFA) may have more beneficial effects for individuals with more pronounced MIR [12]. Since IR and vascular (dys)function are tightly linked [2,13], optimizing the diet to improve glucose homeostasis may translate into superior effects on cardiovascular risk factors as well. To our knowledge, this is the first study to examine whether a more personalized diet to improve glucose homeostasis would also translate to optimal effects on vascular function in individuals with IR. The aim of this study was to determine the effect of a 12-week, more personalized dietary intervention on vascular function and CVD risk factors in individuals with either MIR or LIR.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis current study was executed within the framework of the PERSonalized glucose Optimization through Nutritional intervention (PERSON) study\u0026nbsp;[12]\u0026nbsp;and includes tissue-specific insulin resistant (MIR or LIR), weight stable (3 months ≤3 kg weight gain/loss) individuals (age 40-75), with a BMI between 25-40 kg/m\u003csup\u003e2\u003c/sup\u003e. Main exclusion criteria were: pre-diagnosed diabetes type 2, glucose/lipid altering medications, uncontrolled hypertension, alcohol consumption \u0026gt;14 units/week, smoking, and moderate-to-vigorous physical activity (MVPA) \u0026gt;4 hours/week.A table with all exclusion criteria can be found elsewhere in the design paper of the study\u0026nbsp;[12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn total, 119 participants were included. During the intervention, 7 participants dropped out, resulting in a sample size of 112. Assessments of cardiovascular risk and the OGTT could not be completed for 11 participants due to local COVID-19 lockdowns. Baseline characteristics for the whole group and per intervention arm are shown in \u003cstrong\u003eTable 2.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy design\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs aforementioned, this research was part of the two-center PERSonalized glucose Optimization through Nutritional intervention (PERSON) study\u0026nbsp;[12]. It involves two centers located in the Netherlands, Maastricht University Medical Center+ and Wageningen University \u0026amp; Research (WUR). The complete design and the CONSORT-diagram, which was approved by the local Medical Ethical Committee (NL63768.068.17), is published elsewhere\u0026nbsp;[12]. The PERSON study was registered at a clinical trial register (ClinicalTrials.gov, NCT03708419) and executed according to the Declaration of Helsinki. Vascular measurements were performed at WUR (n=119) only, thus in a subgroup of the total PERSON study population. Before and during week 12 of the intervention, vascular function, cardiovascular risk factors, IR, and disposition index were assessed (\u003cstrong\u003eFigure 1\u003c/strong\u003e), as described in more detail below. The focus of this manuscript is on vascular assessments, which were only performed in this subgroup of the PERSON study. Other results of the PERSON study have been recently published elsewhere\u0026nbsp;[14].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring screening, glucose and insulin values measured during a 7-point OGTT (time points 0, 15, 30, 45, 60, 90, 120) were used to calculate the muscle insulin sensitivity index (MISI) and hepatic insulin sensitivity index (HIRI). Calculations were based on Abdul-Ghani et al.\u0026nbsp;[7]. The modelling of MISI was optimized by O’Donovan et al.\u0026nbsp;[15]. HIRI and MISI have been validated against the golden standard hyperinsulinemic-euglycemic clamp\u0026nbsp;[7,15]. The first blood sample (t=0) was drawn fasted from an intravenous cannula (antecubital vein). The remaining samples were taken after ingestion of a 200 ml 75 g glucose solution (Novolab). Data from The Maastricht study\u0026nbsp;[16], from which a population with characteristics similar to the PERSON participants was selected, was used for MISI/HIRI tertile reference categories. Participants were classified as having MIR if their MISI was within the lowest tertile, and as LIR, if their HIRI was within the highest tertile\u0026nbsp;[12]. Compared to The Maastricht Study, LIR prevalence was found to be lower in the first 163 participants of the PERSON study, wherefore the median HIRI of the PERSON study was used as cutoff thereafter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEducation level, retirement status, and alcohol consumption habits were assessed during screening with questionnaires. A food frequency questionnaire (FFQ, validated, 163-items) assessed habitual dietary intake\u0026nbsp;[17].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiet intervention\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were randomly allocated to follow either Phenotype diet (PhenoDiet) group A (LFHP for LIR, HMUFA for MIR), or PhenoDiet group B (LFHP for MIR, HMUFA for LIR), using center-specific minimization with randomization factors of 1.0 for the LIR/MIR phenotype, and 0.8 for age and sex, and a base probability of 0.7 by means of biased-coin\u0026nbsp;[12]. During the 12-week intervention, participants had to remain weight stable, in order to assess the effect of the diet rather than weight loss. Participants were instructed to maintain their habitual physical activity levels. A more detailed description of the diet, instructions given to participants and exceptions can be found elsewhere\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eDue to COVID-19 restrictions, some aspects of the intervention had to be adjusted, as the weekly visits were not possible anymore: on-site visits were substituted by phone/video calls and key products were delivered to participants at home.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVascular function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCAR was assessed after an overnight fast (\u0026gt;10 h) with ultrasound (Terason uSmart 3300, Burlington, MA, USA) at baseline and during the last week of the intervention. CAR has been associated with coronary artery function, CVD risk, and disease progression in patients with peripheral arterial disease\u0026nbsp;[20,21]. For CAR assessments, additional exclusion criteria applied: angina pectoris, Raynaud disease, chronic pain syndrome affecting the upper extremities, arteriovenous shunt, scleroderma, and heart infarct or heart failure within the last three months. Of the 119 participants, 105 were eligible for vascular function assessment, with 83 participants completing week-12 measurements (6 dropouts, 16 local COVID-19 lockdown). Three ultrasound recordings were excluded due to measurement problems, resulting in a total population for vascular function assessments of 80.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CAR test was performed after a minimum of 10 minutes supine rest. CAR measures the diameter change of the right common carotid artery in response to a 3-min cold pressor test (CPT) (sympathetic stimulus). During CPT, the left hand of the participant was immersed in cold water (≤4°C) up to the wrist. The average diameter of a 1-min baseline recording was compared to the maximum diameter response (in 10 second intervals) during the 3-min CPT, using wall-tracking and edge-detection software\u0026nbsp;[22]. Data were filtered manually for major artefacts, caused for example by swallowing, breathing or probe movement. Analysis was done blinded and an independent assessor reviewed the analyses. In response to the CPT, the carotid artery can dilate or constrict. The direction of reactivity was determined by a positive (dilation) or negative (constriction) area under the curve (CAR\u003csub\u003eAUC\u003c/sub\u003e). CAR% was then defined as the maximum dilation or constriction from baseline, divided by the baseline diameter.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiovascular disease risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCVD risk factors measured before the start of the intervention and during week 12 of the intervention include fasting levels of total cholesterol and high-density lipoprotein (Cobas Pentra C400 with ABX Pentra Cholesterol CP reagens or ABX Pentra HDL Direct, respectively). Blood pressure was measured in sitting position after 5 min rest (dominant arm, automated sphygmomanometer, average of two measurements). The Framingham risk score for cardiovascular disease was calculated as described by D’Agostino et al.\u0026nbsp;[23], based on age, total cholesterol, HDL, treated/untreated systolic blood pressure, diabetes and smoking status. Two measurements of height, weight, and waist-/hip circumference were taken and averaged at each point of assessment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlucose homeostasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 7-point OGTT was performed at baseline and repeated during week 12 of the dietary intervention. MISI and HIRI\u0026nbsp;[7,15]\u0026nbsp;were calculated as follows: MISI= (dGlucose/dt)/insulin [mean during OGTT in pmol/L], with dGlucose/dt being the rate of decay of plasma glucose concentration \u0026nbsp;(mmol/L) during the OGTT. HIRI=glucose\u003csub\u003e0-30\u003c/sub\u003e [AUC in mmol/L*h] x insulin\u003csub\u003e0-30\u003c/sub\u003e [AUC in pmol/L*h].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMatsuda index\u0026nbsp;[24]\u0026nbsp;and Homeostatic Model Assessment of Insulin Resistance (HOMA-IR)\u0026nbsp;[25]\u0026nbsp;were calculated as follows: HOMA-IR: fasting glucose (mmol/L) × (fasting insulin (mU/L)/22.5). Matsuda index: 10,000 ÷ square root of (fasting plasma glucose (mmol/L) x fasting insulin (pmol/L) x (mean glucose T0, T30, T60, T90, T120 (mmol/L) x mean insulin T0, T30, T60, T90, T120 (pmol/L)). In case of one missing timepoint value (N=2), mean glucose/insulin were still calculated with the remaining timepoints.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDisposition index was calculated as: Matsuda index * AUC30 insulin (pmol/L)/AUC30 glucose (mmol/L). AUC30 was calculated as the area under the curve from 0-30 minutes with the trapezoid method.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhysical activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysical activity was measured with the activPAL\u003csub\u003e3\u003c/sub\u003e micro (PAL Technologies Ltd., Glasgow, UK), starting during the baseline measurements and continuing for ~1 week during the first week of the dietary intervention. At the end of the intervention, physical activity was reassessed starting in week 11, continuing until the end of week 12 (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Only ‘free-living’ days (\u003cstrong\u003eFigure 1\u003c/strong\u003e, minimum of 1 weekend + 3 week days), where participants did not visit the university or had to fill in extensive questionnaires, were included in physical activity analysis. ActivPAL data were analyzed with an adapted script based on Winkler at al.\u0026nbsp;[26]. Adaptations were made to include sleep/wake diaries filled in by participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNormally distributed data are presented as mean±SD, non-normal data as median [IQR]. Changes in the outcome variables for the total study population were assessed with a paired t-test or Wilcoxon signed-rank test in case of a non-normal distribution of the delta score (Week 12 - baseline).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnalysis of intervention effects with repeated measures linear mixed models revealed a substantial violation of homoscedasticity for our primary outcome (vascular function). Therefore, differences in delta scores (week 12 - baseline) between interventions (PhenoDiet group A \u003cem\u003eversus\u003c/em\u003e PhenoDiet group B; LFHP \u003cem\u003eversus\u003c/em\u003e HMUFA) were analyzed with linear regression models, corrected for baseline values. In a second model we corrected additionally for age and sex. As participants lost weight during the intervention, which was not intended, we adjusted for weight change in model 3 (\u003cstrong\u003eTable S1-S2\u003c/strong\u003e). In a forth model we corrected for changes in physical activity (\u003cstrong\u003eTable S1-S2\u003c/strong\u003e). To this end, physical activity expressed as % of awake time, as it takes into account the interconnectedness between physical activity and sedentary behavior, meaning that a higher percentage of the day spent in physical activity results in a lower percentage spent in sedentary behavior. Analyses where done in R studio, R version 3.6.2 [27].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eOn average, participants (53.5% female) were 61±7 years old, with a median BMI of 27.6 [26.4;30.0] kg/m\u003csup\u003e2\u003c/sup\u003e. Participant characteristics of the total group (n=101) and per intervention arm can be found in \u003cstrong\u003eTable 2\u003c/strong\u003e. Total physical activity, sedentary time, LIPA and MVPA time did not significantly change during the intervention (\u003cstrong\u003eTable 3\u003c/strong\u003e). There was no significant difference between PhenoDiet group A \u003cem\u003eversus\u003c/em\u003e group B or LFHP \u003cem\u003eversus\u003c/em\u003e HMUFA diets for any of these outcomes (\u003cstrong\u003eTable 4-5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGlucose homeostasis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, dietary intervention increased the Matsuda index, while HOMA-IR decreased significantly and disposition index remained unchanged \u003cstrong\u003e(Table 3)\u003c/strong\u003e. The Matsuda-index (P=0.078) tended to increase more in PhenoDiet group B than A, while HOMA-IR and the disposition index were not significantly different between PhenoDiet groups (\u003cstrong\u003eTable 4\u003c/strong\u003e). The LFHP diet resulted in a greater changes in Matsuda index and HOMA-IR compared to HMUFA (\u003cstrong\u003eTable 5\u003c/strong\u003e), while changes in disposition index were not different between these diets \u003cstrong\u003e(Tables 4-5)\u003c/strong\u003e. Adjustment for weight change or physical activity change did not alter results for HOMA-IR, Matsuda index, or disposition index (\u003cstrong\u003eTables S1-S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVascular function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the total study population, vascular function did not change during the intervention (\u003cstrong\u003eTable 3\u003c/strong\u003e). ∆CAR% and ∆CAR\u003csub\u003eAUC\u003c/sub\u003e were not different between PhenoDiet group A and group B (\u003cstrong\u003eTable 4\u003c/strong\u003e), and we found no significant difference between both diets (LFHP/HMUFA) on CAR (\u003cstrong\u003eTable 5\u003c/strong\u003e). Adjustment for weight change or changes in physical activity did not change these results (\u003cstrong\u003eTables S1-S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCardiovascular risk factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, dietary intervention decreased CVD risk factors(SBP, total cholesterol, HDL, total cholesterol/HDL ratio, \u003cstrong\u003eTable 3\u003c/strong\u003e). The Framingham risk score also decreased significantly (\u003cstrong\u003eTable 3\u003c/strong\u003e). Despite the goal of keeping participants weight stable, body weight decreased by 2.1±2.3 kg (P\u0026lt;0.001), while waist-to-hip ratio did not change significantly (P=0.159). Total cholesterol (P=0.052) tended to decrease more in PhenoDiet group B than A, but changes in other CVD risk factors in were not different between PhenoDiet group A and B (\u003cstrong\u003eTable 4\u003c/strong\u003e). LFHP resulted in a greater decrease in total cholesterol, HDL, and DBP compared to MUFA (\u003cstrong\u003eTable 5\u003c/strong\u003e). After adjustment for weight change or physical activity change, the change in DBP was not significantly different between LFHP and HMUFA (P=0.05, P=0.079, respectively), whilst correction for weight or physical activity did not alter the other outcomes (\u003cstrong\u003eTables S1-S2\u003c/strong\u003e). The decrease in SBP, Framingham risk score, and weight was not different between LFHP \u003cem\u003eversus\u003c/em\u003e HMUFA (\u003cstrong\u003eTable 5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eAs there is a close link between IR and vascular dysfunction\u0026nbsp;[2,13], the primary objective of the present study was to determine whether a more personalized diet, designed to optimize glucose homeostasis, has more pronounced beneficial effects on vascular function and cardiovascular risk factors. \u0026nbsp;We found that consumption of a healthy diet for 12 weeks improved cardiovascular risk factors and IR, but did not significantly affect vascular function and disposition index, in individuals with either MIR or LIR. Moreover, personalized diets according to IR phenotype showed that the Matsuda index and total cholesterol concentrations tended to improve more in PhenoDiet group B versus A, while no significant superiority was found for other outcomes related to vascular function or cardiovascular risk. Interestingly, however, the LFHP diet resulted in greater improvements in several cardiovascular risk factors and measures of IR, compared to HMUFA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the total study population, the 12-week diet intervention improved cardiovascular risk factors and insulin resistance. The Framingham risk score, which is linked to 10-year risk of CVD\u0026nbsp;[23], also decreased. Both diets represent healthy diets, in line with dietary guidelines\u0026nbsp;[28], with improvements in cardiovascular and metabolic health being in line with previous findings\u0026nbsp;[6,29–31]. In the present study, pre-intervention habitual dietary intake of saturated fat (14% of energy intake) and fiber intake (2.5 g/MJ) did not meet the recommendations (saturated fat \u0026lt;10% of energy intake, 3.4 g/MJ fiber). The targeted dietary intake of either diet (HMUFA or LFHP) improved dietary intake of the participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to our expectations, vascular function did not improve, despite significant improvements in insulin resistance and cardiovascular risk factors during the intervention. CAR is associated with cardiovascular risk factors\u0026nbsp;[21], and vascular function is closely linked to IR\u0026nbsp;[2]. However, this link between IR and vascular function is largely investigated in the peripheral vasculature, where insulin stimulates the production of NO, resulting in NO-dependent vasodilation and increased uptake of glucose\u0026nbsp;[2]. Flow-mediated dilation (FMD), a measure of peripheral vascular function, has been shown to improve with diet interventions within 2 months\u0026nbsp;[32,33]. FMD may be more responsive than the carotid reactivity to a diet as both measures reflect a different part of the vascular system (peripheral versus central) or are mediated through distinct pathways (shear stress versus sympathetic nervous system)\u0026nbsp;[34,35]. These differences between both measures of vascular functio\u0026nbsp;may explain why CAR did not significantly change, despite slight but significant improvements in IR. Alternatively, a longer diet intervention may be required for improvements in cardiovascular risk factors and insulin resistance to translate to improvements in CAR. Future studies could benefit from incorporating more direct measures of insulin-mediated vascular function or FMD, together with measures of central vascular function.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to our expectations, linking the tissue-specific IR phenotype (i.e. predominantly liver or muscle IR) to a distinct diet (LFHP, HMUFA) did not result in superior improvements in cardiovascular risk factors or vascular function, except for a trend for improved insulin resistance (Matsuda index) and total cholesterol concentrations in PhenoDiet group B versus A. Furthermore, we did not detect distinct effects between both PhenoDiet groups on the disposition index, which was the primary outcome of the PERSON study\u0026nbsp;[12]. Recently, we published the findings pertaining to the main outcomes of the PERSON study, which included 242 participants, and found significantly greater improvements in glucose homeostasis and insulin sensitivity in PhenoDiet group B versus A [14]. Since we included a subset of the full dataset, this study was not powered to detect changes in glucose homeostasis between the PhenoDiet groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile a healthy diet improved cardiovascular risk factors in the total study population, we found a more pronounced decrease in total cholesterol and DBP in individuals who followed the LFHP diet. Moreover, in contrast to our expectations, both diets reduced HDL concentration. Previous studies show conflicting results regarding the effects of diet on HDL, with increased or unchanged HDL levels being reported after a Mediterranean diet [29,30] and decreased to increased HDL levels after a low-fat diet [29,30,36–38]. The restriction of alcohol consumption during the dietary intervention to\u0026nbsp;≤1 glass/day might have contributed to reductions in HDL, as alcohol consumption can increase HDL levels [39]. However, the decrease in HDL was not significantly different between participants with low (≤3.5 glasses/week) \u003cem\u003eversus\u003c/em\u003e high (≥3.5 glasses/week) baseline alcohol consumption (split at median, P=0.986, data not shown). Despite a reduction in HDL, the total cholesterol/HDL ratio decreased significantly, suggesting a relatively larger decline in total cholesterol than the change in HDL, with no difference between the diets. These changes in blood lipids may have a beneficial impact on CVD risk, as lower total cholesterol/HDL ratio is associated with lower risk for CVD [40,41]. A greater improvement in DBP has previously been reported with a LFHP diet, compared to a Mediterranean diet, in individuals with type 2 diabetes [29]. A bigger (nonsignificant) weight reduction (-2.5 kg [-3.6;-0.5] \u003cem\u003eversus\u003c/em\u003e -1.6 kg [-3.6;0.1]), and (non-significant) increase in physical activity (15.8 min [-44.3;43.8] versus\u0026nbsp;-7.7 min [-50.4;26.0]) with the LFHP diet may, at least partially, contribute to the differences in ∆DBP observed between the LFHP and HMUFA diets. Adjustment for changes in weight or changes in physical activity resulted in non-significant differences between LFHP and HMUFA for ∆DBP, although a trend for difference (P\u0026lt;0.1) remained present. Even small increases in physical activity (even at light intensity), by breaking up prolonged periods of sitting, have previously been shown to be beneficial for blood pressure [42]. The association between weight and blood pressure is moreover well established [43].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA strength of this study is the objective assessment of physical activity before and after the intervention. In the total study population, physical activity did not change significantly, and adjustment for changes in physical activity did not alter our results substantially. This suggests that our results are indeed related to the diet intervention rather than (subconscious) changes in physical activity. A few limitations of this study need to be acknowledged. This study was designed to keep participants weight stable, to be able to attribute outcomes to diet composition rather than weight loss. Despite our efforts to keep participants weight stable, by adjusting energy groups when participants lost weight, participants lost an average of 2 kg during the 12-week intervention. However, results were largely unaltered after adjustment for weight change. Another limitation of our study relates to its power. Some of our outcomes (CVD risk, insulin resistance) represent a subgroup analysis of the PERSON study and may therefore be underpowered [14].\u0026nbsp;However, in previous work, we found that\u0026nbsp;effects of a lifestyle intervention on CAR have previously been seen with a much smaller study population (N=19) [44].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn conclusion, in individuals with LIR or MIR, a healthy diet (LFHP or HMUFA) can improve cardiovascular risk factors and insulin resistance within 12 weeks, while we found no adaptations in the common carotid artery vascular function. Importantly, assigning individuals based on their IR phenotype (LIR or MIR) to a distinct diet tended to further improve insulin sensitivity (Matsuda index) and total cholesterol concentrations, but did not alter other cardiovascular risk factors or vascular function. In addition, comparing both types of diet, we found that LFHP resulted in greater improvements in some markers of CVD risk and IR. Taken together, these results highlight the benefits of diet to improve cardiovascular risk and IR, with our data suggesting that the diet type \u003cem\u003eper se\u003c/em\u003e (LFHP) has larger effects compared to personalization of the diet based on the IR phenotype.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eCLINICAL TRIAL REGISTRATION:\u0026nbsp;The study design was approved by the local Medical Ethical Committee (NL63768.068.17). The PERSON study was registered at a clinical trial register (ClinicalTrials.gov, NCT03708419) and executed according to the Declaration of Helsinki. All study participants provided written consent before participation.\u003c/p\u003e\n\u003cp\u003eFUNDING\u003cem\u003e:\u0026nbsp;\u003c/em\u003eThe project is organized by and executed under the auspices of TiFN, a public -private partnership on precompetitive research in food and nutrition. The authors have declared that no competing interests exist in the writing of this publication. Funding for this research was obtained from the industry partners DSM Nutritional Products, FrieslandCampina, and Danone Nutricia Research; and the Netherlands Organisation for Scientific Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDISCLOSURE\u003cem\u003e:\u0026nbsp;\u003c/em\u003eAuthors report no competing interests.\u003c/p\u003e\n\u003cp\u003eAUTHOR CONTRIBUTIONS\u003cem\u003e:\u0026nbsp;\u003c/em\u003eLW participated in data collection, performed the data analysis, and drafted the manuscript. AG was responsible for execution of the study. GBH was responsible for data management. EEB was project leader and obtained funding for the project. DHJT also obtained funding for the project. EEB, LAA, GHG, EJMF, DHJT co-designed the study. LAA, MTEH, GHG, and DHJT supervised the research activities. All authors participated in the discussion of results and revision of the article. AAll authors have read and approved the \u0026nbsp;final version of the manuscript, and agree with the order of presentation of the authors..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAggregated and individual participant data and associated supporting documents will be made available from the corresponding author upon reasonable request. Individual participant data that underlie the results reported in this article, after deidentification, will be shared upon reasonable request after publication and ending 36 months following provision of the data to researchers who provide a methologically sound proposal. Proposals should be directed to the corresponding author, Dick Thijssen. All remaining data can be found in the Article and Supplementary.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBlaak EE, Antoine J-M, Benton D, Bj\u0026ouml;rck I, Bozzetto L, Brouns F et al (2012) Impact of postprandial glycaemia on health and prevention of disease. Obes Rev 13(10):923\u0026ndash;984\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J, Montagnani M, Koh KK, Quon MJ (2006) Reciprocal Relationships Between Insulin Resistance and Endothelial Dysfunction: molecular and pathophysiological mechanisms. Circulation 113(15):1888\u0026ndash;1904\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalas-Salvad\u0026oacute; J, Martinez-Gonz\u0026aacute;lez M\u0026Aacute;, Bull\u0026oacute; M, Ros E (2011) The role of diet in the prevention of type 2 diabetes. 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Physiol Rep 6(18):e13867\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Mil ACCM, Tymko MM, Kerstens TP, Stembridge M, Green DJ, Ainslie PN et al (2018) Similarity between carotid and coronary artery responses to sympathetic stimulation and the role of α 1 -receptors in humans. J Appl Physiol 125(2):409\u0026ndash;418\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrinton EA, Eisenberg S, Breslow JL (1990) A low-fat diet decreases high density lipoprotein (HDL) cholesterol levels by decreasing HDL apolipoprotein transport rates. J Clin Invest 85(1):144\u0026ndash;151\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsztalos B, Lefevre M, Wong L, Foster TA, Tulley R, Windhauser M et al (2000) Differential response to low-fat diet between low and normal HDL-cholesterol subjects. J Lipid Res 41(3):321\u0026ndash;328\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWycherley TP, Brinkworth GD, Clifton PM, Noakes M (2012) Comparison of the effects of 52 weeks weight loss with either a high-protein or high-carbohydrate diet on body composition and cardiometabolic risk factors in overweight and obese males. Nutr Diabetes 2(8):e40\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Oliveira e Silva ER, Foster D, McGee Harper M, Seidman CE, Smith JD, Breslow JL et al (2000) Alcohol Consumption Raises HDL Cholesterol Levels by Increasing the Transport Rate of Apolipoproteins A-I and A-II. Circulation 102(19):2347\u0026ndash;2352\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKinosian B, Glick H, Garland G (1994) Cholesterol and Coronary Heart Disease: Predicting Risks by Levels and Ratios. Ann Intern Med 121(9):641\u0026ndash;647\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCalling S, Johansson S-E, Wolff M, Sundquist J, Sundquist K (2019) The ratio of total cholesterol to high density lipoprotein cholesterol and myocardial infarction in Women\u0026rsquo;s health in the Lund area (WHILA): a 17-year follow-up cohort study. BMC Cardiovasc Disord 19(1):239\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLarsen RN, Kingwell BA, Sethi P, Cerin E, Owen N, Dunstan DW (2014) Breaking up prolonged sitting reduces resting blood pressure in overweight/obese adults. Nutr Metab Cardiovasc Dis 24(9):976\u0026ndash;982\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarsha DW, Bray GA (2008) Weight Loss and Blood Pressure Control (Pro). Hypertension 51(6):1420\u0026ndash;1045\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuckley BJR, Watson PM, Murphy RC, Graves LEF, Whyte G, Thijssen DHJ (2019) Carotid Artery Function Is Restored in Subjects With Elevated Cardiovascular Disease Risk After a 12-Week Physical Activity Intervention. Can J Cardiol 35(1):23\u0026ndash;26\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eMacronutrient composition\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e\u003cstrong\u003eLFHP\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e\u003cstrong\u003eHMUFA\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u003cstrong\u003eFat\u0026nbsp;\u003c/strong\u003e(Energy %)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e28\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u0026nbsp; \u0026nbsp;Monounsaturated\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e20\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u0026nbsp; \u0026nbsp;Polyunsaturated\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u0026nbsp; \u0026nbsp;Saturated\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u003cstrong\u003eProtein\u0026nbsp;\u003c/strong\u003e(Energy %)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e24\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e14\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u003cstrong\u003eCarbohydrates\u0026nbsp;\u003c/strong\u003e(Energy %)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e42\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e42\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"67.40331491712708%\" valign=\"top\"\u003e\u003cstrong\u003eFiber, g/MJ\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.955801104972377%\" valign=\"top\"\u003e\u0026gt;4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.640883977900552%\" valign=\"top\"\u003e3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"3\" valign=\"top\"\u003eHMUFA, high-monounsaturated fats; LFHP, low-fat, high-protein. Energy % of total energy intake. MJ, megajoule.\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Participant characteristics\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eN=101\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e\u003cstrong\u003eLFHP-PhenoDiet A\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eN=17\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e\u003cstrong\u003eLFHP- PhenoDiet B\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eN=31\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e\u003cstrong\u003eHMUFA- PhenoDiet A\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eN=36\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e\u003cstrong\u003eHMUFA- PhenoDiet B\u003c/strong\u003e\u003cbr\u003e\u003cstrong\u003eN=17\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eAge, years\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e61\u0026plusmn;7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e61\u0026plusmn;4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e62\u0026plusmn;8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e60\u0026plusmn;8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e60\u0026plusmn;8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.671\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eSex, female\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e54 (53.5%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e9 (52.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e18 (58.1%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e19 (52.8%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e8 (47.1%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.907\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e27.6 [26.4; 30.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e26.7 [26.2; 27.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e27.4 [26.3; 29.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e27.6 [26.4; 29.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e29.6 [28.4; 31.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.061\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eStatins, yes\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e5 (5.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e1 (5.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e3 (9.7%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e1 (2.8%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e0 (0.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.569\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eAntihypertensives, yes\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e9 (8.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e1 (5.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e4 (12.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e3 (8.3%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e1 (5.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.892\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eRetired, yes\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e32 (31.7%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e3 (17.6%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e13 (41.9%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e11 (30.6%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e5 (29.4%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.374\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.146838156484458%\" rowspan=\"3\"\u003eEducation level\u003csup\u003ea\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.109324758842444%\" valign=\"top\"\u003eLow\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e4 (4.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e2 (11.8%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e2 (6.5%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e0 (0.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e0 (0.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" rowspan=\"3\"\u003e0.327\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.5%\"\u003eInt\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e39 (39.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.92105263157895%\" valign=\"top\"\u003e8 (47.1%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.736842105263158%\" valign=\"top\"\u003e12 (38.7%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.42105263157895%\" valign=\"top\"\u003e14 (40.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.736842105263158%\" valign=\"top\"\u003e5 (29.4%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.5%\" valign=\"top\"\u003eHigh\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.68421052631579%\" valign=\"top\"\u003e57 (57.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.92105263157895%\" valign=\"top\"\u003e7 (41.2%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.736842105263158%\" valign=\"top\"\u003e17 (54.8%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.42105263157895%\" valign=\"top\"\u003e21 (60.0%)\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.736842105263158%\" valign=\"top\"\u003e12 (70.6%)\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eTotal energy, kcal\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e2178.0\u0026plusmn;589.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e1994.1 [1749.0; 2368.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e1955.5 [1839.2; 2229.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e2251.3 [1721.8; 2524.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e2491.4 [1858.8; 3114.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.257\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eCarbohydrates, energy%\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e42.3 [39.4; 45.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e41.2 [37.8; 47.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e42.1 [40.6; 45.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e42.6 [37.1; 45.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e42.5 [40.0; 43.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.690\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eProtein, energy%\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e15.4 [14.4; 16.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e15.6\u0026plusmn;1.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e15.9\u0026plusmn;2.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e15.6\u0026plusmn;2.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e15.2\u0026plusmn;1.9\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.801\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eFat, energy%\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e36.5 [34.1; 40.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e35.4 [33.0; 43.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e36.5 [34.4; 38.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e36.5 [33.2; 40.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e36.4 [34.9; 42.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.868\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eSaturated fat, energy %\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e13.9 [12.0; 15.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e13.6 [11.3; 15.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e13.9 [11.9; 15.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e14.2 [12.3; 15.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e13.9 [12.3; 16.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.927\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eFiber, g/MJ\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e2.5\u0026plusmn;0.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e2.5 [2.1; 2.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e2.5 [2.1; 2.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e2.5 [2.0; 3.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e2.4 [2.0; 2.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.946\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.256162915326904%\" colspan=\"2\" valign=\"top\"\u003eAlcohol, glasses/week\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.146838156484458%\" valign=\"top\"\u003e3.5 [0.9; 6.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.041800643086816%\" valign=\"top\"\u003e4.0 [0.0; 9.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e3.0 [0.5; 6.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.005359056806002%\" valign=\"top\"\u003e3.0 [2.0; 5.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.077170418006432%\" valign=\"top\"\u003e4.0 [0.5; 6.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.395498392282958%\" valign=\"top\"\u003e0.960\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\" valign=\"top\"\u003eLFHP, low-fat high-protein diet; HMUFA, high-monounsaturated fatty acid diet; BMI, body mass index.\u0026nbsp;\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eLow: no education, primary education, lower/preparatory vocational education, lower general secondary education, medium: int, intermediate vocational education, higher general senior secondary education, pre-university secondary education, high: higher vocational education, university. For education level and glasses alcohol: total N=100. Normally distributed data are presented as mean\u0026plusmn;SD, non-normal data as median [IQR].\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eOutcomes at\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ebaseline and week 12: total study population\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\"\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e\u003cstrong\u003eTotal group N=80\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\"\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eCAR%, %\u003c/strong\u003e\u003cbr\u003eN=80\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e2.39 [1.37; 3.34]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.485\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e2.23 [1.25; 3.00]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.18 [-1.40; 1.08]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eCAR\u003csub\u003eAUC\u003c/sub\u003e, cms\u003c/strong\u003e\u003cbr\u003eN=80\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e1.57 [0.55; 2.33]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.783\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e1.42 [0.52; 1.99]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.15 [-0.90; 0.88]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eSBP, mmHg\u003c/strong\u003e\u003cbr\u003eN=100\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e124 [114; 136]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e118 [110; 128]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-5\u0026plusmn;10\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eDBP, mmHg\u003c/strong\u003e\u003cbr\u003eN=100\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e73\u0026plusmn;10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e71\u0026plusmn;10.0\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-2\u0026plusmn;7\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eTC, mmol/L\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e5.45\u0026plusmn;1.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e4.83\u0026plusmn;0.93\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.62 [-0.89; -0.36]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eHDL-C, mmol/L\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e1.30 [1.11; 1.48]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e1.22 [1.06; 1.36]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.09\u0026plusmn;0.11\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eTC/HDL-C, ratio\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e4.31\u0026plusmn;1.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e4.08\u0026plusmn;1.02\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.23\u0026plusmn;0.38\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eFRS\u003c/strong\u003e\u003cbr\u003eN=96\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e11.6\u0026plusmn;3.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e10.4\u0026plusmn;3.8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-1.0 [-2.0; 0.0]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eWeight, kg\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e85.6\u0026plusmn;10.8\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e83.5\u0026plusmn;10.8\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-2.1\u0026plusmn;2.3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eWHR\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e0.960 [0.910; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.159\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e0.950 [0.890; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.006\u0026plusmn;0.042\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eMatsuda\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e13.1 [9.7; 17.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e\u0026lt;0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e14.0 [11.1; 20.1]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e1.3 [-0.8; 4.4]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eHOMA-IR\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e1.6 [1.3; 2.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.001\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e1.4 [1.1; 2.0]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.1 [-0.4; 0.1]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eDisposition index\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e420.1 [293.8; 647.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.362\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e438.2 [298.4; 627.3]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e\u0026nbsp; 16.1\u0026plusmn;177.0\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eSitting, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e9.5\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.586\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e9.5\u0026plusmn;1.5\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e0.1\u0026plusmn;1.3\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003ePA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e6.2\u0026plusmn;1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.315\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e6.1\u0026plusmn;1.7\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.1\u0026plusmn;1.2\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eLIPA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e5.0\u0026plusmn;1.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.111\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e4.9\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e-0.2\u0026plusmn;1.0\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.7027027027027%\" rowspan=\"3\"\u003e\u003cstrong\u003eMVPA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"18.01801801801802%\"\u003eBaseline\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.108108108108105%\"\u003e1.2 [0.9; 1.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.17117117117117%\" rowspan=\"3\"\u003e0.338\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eWeek 12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e1.2 [0.8; 1.5]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"35.242290748898675%\"\u003eDelta\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"64.75770925110132%\"\u003e0.0 [-0.2; 0.3]\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"4\"\u003eCAR, carotid artery reactivity; AUC, area under the curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL, low-density lipoprotein; FRS, Framingham risk score; WHR, waist-to-hip ratio; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; PA, physical activity; LIPA, light-intensity PA; MVPA, moderate-to-vigorous PA. Normally distributed data are presented as mean\u0026plusmn;SD, non-normal data as median [IQR]. P-value for differences between baseline and week 12.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"left\" width=\"941\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eChanges in vascular function, cardiovascular risk factors, glucose homeostasis and physical activity: PhenoDiet group A (N=53)\u0026nbsp;\u003cem\u003eversus\u003c/em\u003e PhenoDiet group B (N=48).\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.319148936170214%\" rowspan=\"2\"\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.361702127659575%\" rowspan=\"2\"\u003e\u003cstrong\u003eDiet group\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" rowspan=\"2\"\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.06382978723404%\" rowspan=\"2\"\u003e\u003cstrong\u003eWeek 12\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.042553191489361%\" rowspan=\"2\"\u003e\u003cstrong\u003e∆\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"21.06382978723404%\" colspan=\"2\"\u003e\u003cstrong\u003eModel 1*\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"20.106382978723403%\" colspan=\"2\"\u003e\u003cstrong\u003eModel 2*\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.701030927835053%\"\u003e\u0026beta;\u0026nbsp;[95% CI]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"32.21649484536083%\"\u003e\u0026beta;\u0026nbsp;[95% CI]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eCAR%, %\u003c/strong\u003e\u003cbr\u003eN=80\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e2.48 [1.35; 3.91]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e2.24 [1.32; 2.74]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.27 [-1.43; 0.73]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e2.33 [1.38; 2.96]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e2.07 [1.21; 3.16]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.17 [-1.29; 1.31]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e0.03 [-0.84\u0026ndash;0.89]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.952\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-0.06 [-0.92\u0026ndash;0.81]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.899\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eCAR\u003csub\u003eAUC\u003c/sub\u003e, cm*s\u003c/strong\u003e\u003cbr\u003eN=80\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e1.67 [0.48; 2.91]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e1.79 [0.53; 2.20]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e0.19 [-0.78; 1.10]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e1.54 [0.79; 2.17]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e1.05 [0.53; 1.86]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.34 [-0.98; 0.80]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e-0.30 [-0.98\u0026ndash;0.38]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.376\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-0.37 [-1.04\u0026ndash;0.31]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.287\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eSBP, mmHg\u003c/strong\u003e\u003cbr\u003eN=100\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e128\u0026plusmn;16\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e120\u0026plusmn;12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-8\u0026plusmn;11\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e122\u0026plusmn;16\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e119\u0026plusmn;15\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-3\u0026plusmn;10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e2.84 [-0.60\u0026ndash;6.28]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.105\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e2.76 [-0.7\u0026ndash;6.23]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.117\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eDBP, mmHg\u003c/strong\u003e\u003cbr\u003eN=100\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e74\u0026plusmn;10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e71\u0026plusmn;9\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-3\u0026plusmn;7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e73\u0026plusmn;11\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e71\u0026plusmn;11\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-1\u0026plusmn;6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e1.32 [-1.13\u0026ndash;3.76]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.287\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e1.06 [-1.38\u0026ndash;3.50]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.392\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eTC, mmol/L\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e5.44\u0026plusmn;1.06\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e4.72\u0026plusmn;0.92\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.66 [-0.95; -0.39]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e5.47\u0026plusmn;1.03\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e4.92\u0026plusmn;0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.59 [-0.85; -0.27]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e0.18 [-0.00\u0026ndash;0.37]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.052\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e0.18 [-0.01\u0026ndash;0.37]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.059\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eHDL-C, mmol/L\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e1.30 [1.10; 1.47]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e1.18 [0.99; 1.33]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.10\u0026plusmn;0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e1.30 [1.16; 1.49]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e1.23 [1.07; 1.38]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.07\u0026plusmn;0.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e0.04 [-0.01\u0026ndash;0.08]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.103\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e0.04 [-0.01\u0026ndash;0.08]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.094\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eTC/HD-C, ratio\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e4.34\u0026plusmn;1.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e4.10\u0026plusmn;1.07\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.25\u0026plusmn;0.40\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e4.28\u0026plusmn;1.09\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e4.06\u0026plusmn;0.97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.21\u0026plusmn;0.36\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e0.03 [-0.12\u0026ndash;0.17]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.723\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e0.02 [-0.12\u0026ndash;0.16]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.813\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eFRS\u003c/strong\u003e\u003cbr\u003eN=96\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd 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width=\"14.57345971563981%\"\u003e0.60 [-0.17\u0026ndash;1.37]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.124\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e0.48 [-0.22\u0026ndash;1.18]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.179 \u0026nbsp;\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eWeight, kg\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e86.6\u0026plusmn;9.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e\u0026nbsp; 84.1\u0026plusmn;9.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-2.5\u0026plusmn;2.2\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e84.7\u0026plusmn;12.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e83.0\u0026plusmn;12.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-1.7\u0026plusmn;2.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e0.74 [-0.17\u0026ndash;1.66]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.110\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e0.63 [-0.28\u0026ndash;1.55]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.173\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eWHR\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e0.965 [0.910; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e0.945 [0.898; 1.002]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.004\u0026plusmn;0.046\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e0.960 [0.910; 1.000]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e0.950 [0.880; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.007\u0026plusmn;0.038\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e-0.00 [-0.02\u0026ndash;0.01]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.563\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-0.01 [-0.02\u0026ndash;0.01]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.336 \u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eMatsuda\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e12.2 [9.2; 15.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e14.8 [11.2; 20.7] \u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e2.1 [0.0; 4.8]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e13.4 [10.1; 18.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e13.5 [11.1; 19.8]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e0.4 [-1.3; 4.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e-1.69 [-3.58\u0026ndash;0.20]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.078\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-1.58 [-3.48\u0026ndash;0.31]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.101 \u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eHOMA-IR\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e1.7 [1.4; 2.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e1.4 [1.1; 2.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.2 [-0.6; 0.1]\u003cbr\u003e\u003c/td\u003e\n 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width=\"7.5829383886255926%\"\u003e0.560\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eDisposition index\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e416.4 [253.5; 566.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;432.1 [280.9; 589.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e22.2\u0026plusmn;169.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n 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width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e9.3\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e9.3\u0026plusmn;1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e0.0 [-0.7; 0.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e9.6\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e9.7\u0026plusmn;1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.1 [-0.9; 0.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e0.19 [-0.30\u0026ndash;0.68]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.442\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e0.18 [-0.31\u0026ndash;0.67]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.467\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003ePA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e6.3\u0026plusmn;1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e6.4\u0026plusmn;1.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e0.2 [-0.8; 0.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e6.1\u0026plusmn;1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e5.9\u0026plusmn;1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.1 [-0.8; 0.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e-0.31 [-0.79\u0026ndash;0.18]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.213\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-0.29 [-0.77\u0026ndash;0.20]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.241\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eLIPA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e5.1 [4.3; 6.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e4.9 [4.1; 5.8]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.0 [-0.6; 0.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e4.9 [4.1; 5.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e4.8 [3.9; 5.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.2 [-0.6; 0.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e-0.25 [-0.66\u0026ndash;0.15]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.220\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-0.24 [-0.64\u0026ndash;0.17]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.252\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.30818278427205%\" rowspan=\"2\"\u003e\u003cstrong\u003eMVPA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"4.357066950053135%\"\u003eB\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e1.2 [0.9; 1.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.046758767268862%\"\u003e1.2 [1.0; 1.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.027630180658873%\"\u003e-0.0 [-0.2; 0.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.071200850159405%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"8.076514346439957%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.28374070138151%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.801275239107333%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.85781990521327%\"\u003eA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e1.2 [0.9; 1.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.89099526066351%\"\u003e1.1 [0.8; 1.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.639810426540285%\"\u003e-0.0 [-0.2; 0.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.57345971563981%\"\u003e-0.05 [-0.20\u0026ndash;0.10]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.004739336492891%\"\u003e0.491\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.810426540284361%\"\u003e-0.05 [-0.20\u0026ndash;0.10]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.5829383886255926%\"\u003e0.523\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003eCAR, carotid artery reactivity; AUC, area under the curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL, high-density lipoprotein; FRS, Framingham risk score; WHR, waist-to-hip ratio; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; PA, total physical activity, LIPA, light-intensity physical activity; MVPA, moderate-to vigorous physical activity. Normally distributed data are presented as mean\u0026plusmn;SD, non-normal data as median [IQR]. *Linear regression models testing differences in the change (week 12 minus baseline) in the outcome variable between PhenoDiet group A and group B. Model 1: corrected for baseline values; Model 2: corrected for baseline values, age, sex.\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"948\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003e\u003cstrong\u003eTable 5.\u0026nbsp;\u003c/strong\u003eChanges in vascular function, cardiovascular risk factors, glucose homeostasis and physical activity: LFHP (N=48)\u0026nbsp;\u003cem\u003eversus\u003c/em\u003e HMUFA (N=53) diet.\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\" rowspan=\"2\"\u003e\u003cstrong\u003eDiet\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\" rowspan=\"2\"\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\" rowspan=\"2\"\u003e\u003cstrong\u003eWeek 12\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\" rowspan=\"2\"\u003e\u003cstrong\u003e∆\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.494204425711274%\" colspan=\"2\"\u003e\u003cstrong\u003eModel 1*\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"19.915700737618547%\" colspan=\"2\"\u003e\u003cstrong\u003eModel 2*\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.62032085561497%\"\u003e\u0026beta;\u0026nbsp;[95% CI]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.844919786096256%\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"33.155080213903744%\"\u003e\u0026beta;\u0026nbsp;[95% CI]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.379679144385026%\"\u003eP-value\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eCAR%, %\u003c/strong\u003e\u003cbr\u003eN=80\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e2.40 [1.30; 3.79]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.89 [1.25; 2.59]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-0.28 [-1.45; 1.03]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e2.36 [1.40; 3.00]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e2.52 [1.32; 3.34]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e0.10 [-1.13; 1.20]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.40 [-0.46\u0026ndash;1.26]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.362\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.36 [-0.51\u0026ndash;1.23]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.410\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eCAR\u003csub\u003eAUC\u003c/sub\u003e, cm*s\u003c/strong\u003e\u003cbr\u003eN=80\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.48 [0.48; 2.90]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.00 [0.36; 1.89]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e0.08 [-0.81; 0.56]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.68 [0.78; 2.25]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.68 [0.67; 2.06]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.20 [-0.91; 0.95]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.34 [-0.34\u0026ndash;1.02]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.324\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.35 [-0.34\u0026ndash;1.03]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.315\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eSBP, mmHg\u003c/strong\u003e\u003cbr\u003eN=100\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e123 [111; 134]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e117 [108; 124]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-6\u0026plusmn;11\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e125 [117; 136]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e121 [114; 132]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-4\u0026plusmn;10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e3.28 [-0.12\u0026ndash;6.67]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.058\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e3.30 [-0.15\u0026ndash; 6.75]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.061\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eDBP, mmHg\u003c/strong\u003e\u003cbr\u003eN=100\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e72\u0026plusmn;9\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e69\u0026plusmn;9\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-3\u0026plusmn;7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e74\u0026plusmn;12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e73\u0026plusmn;10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-1\u0026plusmn;7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e2.73 [0.34\u0026ndash;5.13]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.026\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e2.48 [0.07\u0026ndash;4.89]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.044\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eTC, mmol/L\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e5.49\u0026plusmn;1.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e4.73\u0026plusmn;0.94\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-0.68 [-1.00; -0.44]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e5.42\u0026plusmn;1.04\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;4.91\u0026plusmn;0.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\" valign=\"top\"\u003e-0.57 [-0.82; -0.20]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.24 [0.06\u0026ndash;0.42]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.011\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e\u0026nbsp; \u0026nbsp;0.23 [0.05\u0026ndash;0.42]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.014\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eHDL-C, mmol/L\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.33 [1.13; 1.48]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.23 [1.04; 1.35]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-0.11\u0026plusmn;0.10\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.30 [1.08; 1.48]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.22 [1.07; 1.37]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.06\u0026plusmn;0.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.05 [0.01\u0026ndash;0.09]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.018\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.05 [0.01\u0026ndash;0.10]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.013\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eTC/HDL-C, ratio\u003c/strong\u003e\u003cbr\u003eN=97\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e4.32\u0026plusmn;1.12\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e4.09\u0026plusmn;1.11\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-0.23\u0026plusmn;0.42\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e4.30\u0026plusmn;1.08\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e4.07\u0026plusmn;0.93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.23\u0026plusmn;0.34 \u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e-0.00 [-0.15\u0026ndash;0.14]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.968\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e-0.02 [-0.16\u0026ndash;0.12]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.800\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eFRS\u003c/strong\u003e\u003cbr\u003eN=96\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e11.5\u0026plusmn;3.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e10.1\u0026plusmn;3.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-1.0 [-2.0; 0.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e11.7\u0026plusmn;4.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e10.6\u0026plusmn;4.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.5 [-2.0; 0.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.32 [-0.46\u0026ndash;1.09]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.420\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.32 [-0.39\u0026ndash;1.03]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.374\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eWeight, kg\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e83.6\u0026plusmn;10.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e81.3\u0026plusmn;10.0\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-2.5 [-3.6; -0.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; 87.4\u0026plusmn;11.1\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e85.5\u0026plusmn;11.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-1.6 [-3.6; 0.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.41 [-0.52\u0026ndash;1.35]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.383\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.37 [-0.56\u0026ndash;1.30]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.429\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eWHR\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e0.950 [0.910; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e0.935 [0.880; 1.002]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-0.007\u0026plusmn;0.045\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e0.970 [0.910; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e0.960 [0.910; 1.010]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.005\u0026plusmn;0.039\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.00 [-0.01\u0026ndash;0.02]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.743\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.00 [-0.01\u0026ndash;0.02]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.883\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eMatsuda\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e13.3 [10.1; 17.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e17.0 [11.9; 22.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e2.1 [0.5; 6.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e13.0 [9.7; 16.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e12.7 [10.5; 17.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e0.2 [-1.3; 3.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e-2.73 [-4.56\u0026ndash;(-0.90)]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.004\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e\u0026nbsp; 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2.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.7 [1.3; 2.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.0 [-0.4; 0.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.26 [0.04\u0026ndash;0.49]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.021\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.24 [0.01\u0026ndash;0.47]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.038\u003csup\u003e#\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eDisposition index\u003c/strong\u003e\u003cbr\u003eN=101\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e433.4 [257.2; 732.1]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e436.0 [284.6; 654.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e26.0 [-80.4; 94.9]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e397.0 [309.5; 574.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e443.1 [299.8; 597.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e23.0 [-85.5; 105.2]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e-2.71 [-68.78\u0026ndash;63.36]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.935\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.89 [-66.31\u0026ndash;68.09]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.979 \u0026nbsp;\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eSitting, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e9.4\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e9.2\u0026plusmn;1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e-0.0 [-1.0; 0.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e9.6\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e9.8\u0026plusmn;1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.0 [-0.6; 1.0]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e0.44 [-0.04\u0026ndash;0.92]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.071\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e0.42 [-0.06\u0026ndash;0.90]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.088\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003ePA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e6.2\u0026plusmn;1.6\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e6.3\u0026plusmn;1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e0.3 [-0.7; 0.7]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e6.2\u0026plusmn;1.5\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e5.9\u0026plusmn;1.7\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.1 [-0.8; 0.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e-0.35 [-0.82\u0026ndash;0.13]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.155\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e-0.31 [-0.80\u0026ndash;0.17]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.204\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eLIPA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e5.0\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e5.0\u0026plusmn;1.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e0.1 [-0.6; 0.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e5.0\u0026plusmn;1.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e4.7\u0026plusmn;1.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e-0.2 [-0.6; 0.3]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e-0.30 [-0.70\u0026ndash;0.10]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.145\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e-0.27 [-0.68\u0026ndash;0.14]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.196\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.69441517386723%\" rowspan=\"2\"\u003e\u003cstrong\u003eMVPA, h\u003c/strong\u003e\u003cbr\u003eN=93\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.533192834562698%\"\u003eLFHP\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.2 [0.9; 1.4]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.489989462592202%\"\u003e1.2 [0.9; 1.6]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.38250790305585%\"\u003e0.1\u0026plusmn;0.3\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.855637513171759%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.638566912539515%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.066385669125395%\"\u003eREF\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.8493150684931505%\"\u003e-\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.234539089848308%\"\u003eHMUFA\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.2 [0.9; 1.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.152858809801632%\"\u003e1.1 [0.8; 1.5]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.819136522753793%\"\u003e0.0\u0026plusmn;0.4\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.235705950991832%\"\u003e-0.06 [-0.21\u0026ndash;0.09]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.3512252042007%\"\u003e0.457\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.469078179696616%\"\u003e-0.05 [-0.21\u0026ndash;0.10]\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.584597432905484%\"\u003e0.498\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"9\"\u003eLFHP, low-fat, high-protein diet; HMUFA, high-monounsaturated fatty acid diet; CAR, carotid artery reactivity; AUC, area under the curve; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; HDL, high-density lipoprotein; FRS, Framingham risk score; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; PA, total physical activity, LIPA, light-intensity physical activity; MVPA, moderate-to vigorous physical activity. \u0026nbsp;Normally distributed data are presented as mean\u0026plusmn;SD, non-normal data as median [IQR]. *Linear regression models testing differences in the change (week 12 minus baseline) in the outcome variable between LFHP and HMUFA diets. Model 1: corrected for baseline values; Model 2: corrected for baseline values, age, sex.\u0026nbsp;\u003csup\u003e#\u003c/sup\u003eFor HOMA-IR, significance was driven by two participants. Exclusion resulted in non-significant differences (P=0.158). \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"nutrition, diet, cardiovascular, insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-4162501/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4162501/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective. \u003c/strong\u003eGiven the interaction between insulin resistance (IR) and cardiovascular risk, we examined whether a personalized diet according muscle insulin-resistant (MIR) or liver insulin-resistant (LIR) phenotypes improves vascular function and cardiovascular disease risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods. \u003c/strong\u003eIndividuals were randomized to a personalized phenotype diet (PhenoDiet) A or B and followed a 12-week low-fat, high-protein (LFHP) diet or high-monounsaturated fatty acid (HMUFA) diet (PhenoDiet A; MIR/HMUFA-LIR/LFHP; PhenoDiet B: MIR/LFHP-LIR/HMUFA). We included 101 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults. \u003c/strong\u003eDietary interventions decreased blood pressure, total cholesterol, HDL-cholesterol and the Framingham risk score (all P\u0026lt;0.05), improved IR ((Matsuda index, HOMA-IR) P\u0026lt;0.001), but not vascular function (P=0.485). Changes in outcome parameters were not significantly different between PhenoDiet groups. The LFHP diet resulted in more pronounced improvements in cholesterol, DBP, and IR compared to the HMUFA diet (all P\u0026lt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion. \u003c/strong\u003eA 12-week healthy diet improves metabolic and cardiovascular outcomes, but not vascular function in IR adults with overweight or obesity. Whilst the LFHP diet resulted in greater improvements in cardiometabolic risk markers than the HMUFA diet, we found no significant differences between the PhenoDiet groups.\u003c/p\u003e","manuscriptTitle":"Impact of a 12-week personalized dietary intervention on vascular function and cardiovascular risk factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-26 02:07:27","doi":"10.21203/rs.3.rs-4162501/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"743e5221-0293-4a61-9866-bef3709dac3d","owner":[],"postedDate":"April 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31053684,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity"},{"id":31053685,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Pre-diabetes"}],"tags":[],"updatedAt":"2024-06-28T14:30:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-26 02:07:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4162501","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4162501","identity":"rs-4162501","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}