Dietary intake, Nutritional status, and Health outcomes among Vegan, Vegetarian, and Omnivore families: Results from the Observational Study

doi:10.21203/rs.3.rs-4700951/v1

Plant-based diets are growing in popularity because of their perceived environmental and health benefits. However, they may be associated with safety risks, that may cluster within families. Here, we conducted a cross-sectional study of 95 families (47 vegan [VN], 23 vegetarian [VG], and 25 omnivore [OM]), including 187 adults, 65 children >3 years, and 77 children <3 years old. Growth, cardiovascular health, bone turnover, iodine, and vitamin/micronutrient status were specifically assessed. We found no significant differences in children’s growth characteristics in children between the dietary groups. Better cardiometabolic indices in VN (LDL and total cholesterol) were found as early as in children >3 years of age. In addition, OM had a higher BMI, diastolic blood pressure, and lower fat-free mass in adults. Higher bone turnover (P1NP) was found in older children and adult VN, where it was related to higher PTH levels. Paradoxically, vitamin D levels were generally higher in VN. Lower urinary iodine, associated with lower intake in VN was found across all age strata, with no effect on TSH. Mixed models suggested that namely height, micronutrient status (Se, Zn, and urinary iodine), and vitamin levels (folate, B12, and D) are clustered within families. Our results show that dietary habits significantly impact on nutritional biomarkers, with family influence playing an important role. Although no serious adverse effects of the diet were found, iodine status and bone health in vegans warrant further research.

Health sciences/Medical research/Epidemiology

Health sciences/Health care/Public health/Epidemiology

The global trend to reduce the environmental burden of food production and tackle the obesity pandemic is being followed by a reduction in the consumption of foods of animal origin. The growing trend towards plant-based diets is increasingly evident in many regions from the Eastern European block including the Czech Republic. This trend is particularly more pronounced among younger demographic groups. According to recent surveys, 3% of Czech consumers identify themselves as vegan, 7% as vegetarian, and a remarkable 25% as voluntarily reducing their intake of animal-based foods, significantly more compared to previous years^1,2. As the eating habits are shared among the households, there is also a growing number of children on these diets.

While plant-based diets were shown to be associated with favorable health effects and reduced risk of various non-communicable diseases^3,4, they also carry the potential risks of both specific nutrient deficiencies (such as vitamins B12 and D, and the minerals iodine, zinc, calcium, iron, and selenium) and total caloric and high-quality protein intake⁵. This concern is particularly acute in critical developmental stages such as early childhood^6,7. The inadequacy of plant-based diets was related to specific concerns of growth and development^8,9, bone health^10,11, and iodine deficiency^5,12 across different age groups.

Families often share dietary patterns, so dietary choices made within a household have a collective effect on all members, including children¹³. More importantly, dietary habits established in childhood can significantly shape health outcomes later in life, underscoring the importance of early dietary choices^13,14. The investigation of plant-based diets within family units, encompassing both children and parents, is integral to understanding the impact of these diets.

The KOMPAS study (Cohort prospective study of emerging nutritional factors among families) is a prospective cohort single-center study, that aims to investigate the health effects of different dietary patterns within family units, tracking these effects from childhood into adulthood. To this end, we performed a cross-sectional comparison of baseline data in three groups (vegan, vegetarian, and omnivore) of families with distinct dietary habitsto 1/ describe the differences in growth and anthropometric characteristics, and nutritional status, 2/ differences in health-related outcomes, 3/ differences in dietary intake of critical nutrients, and 4/ explore the interrelations between observed variables, namely the predictive potential of a dietary group and family on the observable variations in these variables.

Description of the Study Groups

For the study overview see the Study flow-chart Figure 1 and Table 1.

A total of 95 families (OM=25, VG=23,, VN=47) consisting of 187 adults, 65 children >3 years of age and 77 children <3 years of age were enrolled and examined in a cross-sectional setting. Adult vegans were on exclusive plant-based diets on average ≈for 7.4 years whereas vegetarians ≈12 years, and all children were on each respective diet from birth. Overall participants in the study were healthy. Twenty adult participants reported history of a thyroid disease (OM=3, VG=6, VN=11), of whom 10 were treated with thyroid hormone substitution. One participant had type 1 diabetes on insulin (VG=1), two participants had hypertension compensated on the treatment (VG=2, OM=1) and nine participants reported a history of hyperlipoproteinemia (VN=5, VG=1, OM=3) compensated in all without treatment. Three vegans reported a history of osteoporosis and there was a history of fractures in 87 subjects distributed evenly across groups (OM=27, VG=23, VN=27) that are described in detail in Suppl. Table 1. Self-reported prevalence of allergy was significantly lower in adult VN (13%) compared with adult OM (34%, p=0.003) and adult VG (27%, p=0.049). Parents reported lower allergy incidence in VN children < 3 years old compared with OM (2.5 vs 23%, p=0.018). Nine children had a history of atopy (OM=6, VG=2, VN=1) and five had a history of food intolerance (OM=4, VG=1). Psychomotor developmental delay was reported in three children (OM=1, VG=1, VN=1 together with autism and mild mental challenge). No thyroid disease or an autoimmune disease was reported in children across all groups and age strata. Other reported diseases in children < 3 years old included: valve insufficiency (OM=1), umbilical hernia (OM=1), neutropenia (OM=1), sideropenic anemia (OM=1); and in children >3 years old ADHD (VN=1); persistent foramen ovale (VN=2); persistent foramen ovale and epilepsy (VN=1).

Anthropometrics and clinical characteristics

Diet groups were compared separately across three age groups: (i) infants/toddlers (children < 3 years old), (ii) pre-schoolers/schoolers (children > 3 years old), and (iii) adults. Three different statistical techniques were employed: 1/ Kruskal-Wallis (KW) Test and Mann-Whitney U (MWU) Posthoc Test: These were used for raw (unadjusted) comparisons. 2/ Quantile Regression (QR): This method provided effect estimates adjusted for potential confounders such as age (log-transformed for children), sex, and breastfeeding (in children groups). It included cluster bootstrap to account for data dependence within families and modeled not only median values but also the 20th and 80th percentiles. 3/ Robust Mixed-Effects Models (rLME): These models provided adjusted effect estimates (mean values) and quantified the importance of family influence. rLMEs were also fitted for the merged dataset of all children to obtain a more robust estimation of the characteristics shared within families (Summarized in Tables 2-4 and Suppl. Tables 11-13). The previously observed raw median differences were considered 'supported' by the adjusted models when both the median from QR and the mean from rLME showed corresponding differences, and 'partially supported' when only one of these measures provided support.

We found no significant differences in anthropometric and growth characteristics in the children < 3 years old(Table 2), though the VN group in this age stratum tended to have lower median values of height, and weight, albeit normal BMI of values expressed as percentiles of population-based appropriate values per age. Four children (OM=1; VN=2; VG=1) below the third height percentile and two VN children below the weight percentile were identified. In vegans, we found significantly higher serum concentrations of active B12 compared to both OM and VG, further supported by adjusted models for VN vs. VG comparison. Similarly, vegans showed lower levels of homocysteine and methylmalonic acid compared with both OM and VG groups, further supported by the adjusted models. Vegan children had also significantly lower urinary iodine concentration (not further supported with adjusted models) and higher serum vitamin D concentration compared with omnivores (partially supported). Vegan and vegetarian children had higher levels of folate compared with the OM group, further supported with adjusted models. Of note, medians of the differing parameters felt in a normal range in all groups. Nevertheless, twenty children met the criteria for iodine deficiency (i.e., UIC < 100 µg/l; OM=1, VG=3, VN=16), whereby mean TSH=2,25 MU/L, i.e. in the normal range. Out of the sixteen vegan children, two children met the criteria for severe iodine deficiency (i.e., UIC <20 µg/l). Eight children with elevated active B12 levels (i.e. holotranscobalamin >169,2 pmol/l; VG=1, VN=7) were identified. On the contrary, vitamin B12 deficiency (i.e., holotranscobalamin <27,4 pmol/l) was identified in only two children (OM=1, VN=1). Sixteen children met the criteria for mild vitamin D depletion (i.e. 25(OH)D <75 nmol/l; OM=7; VG=2; VN=7).

Among the groups of children > 3 years old (Table 3) we observed no differences in anthropometric and growth characteristics. Three children (OM=1; VN=1; VG=1) below the third height percentile and two vegan children below the third weight percentile were identified. In vegan children, we found significantly lower serum concentrations of total and LDL cholesterol as well as higher P1NP and vitamin D status, though these differences were generally not supported with adjusted models. Vegans had lower urinary iodine concentrations, (not further supported with adjusted models), and higher folate levels which remained significant after adjustment for covariates included in the models. Serum B12 showed a trend towards significant differences in vegans having the highest levels while vegetarians having the lowest levels. Of note, medians of the differing parameters felt in a normal range in all groups. Nevertheless, four children had LDL cholesterol levels below the lower reference limit (OM=1, VG=1, VN=2), twenty-five children, mostly on an omnivorous diet, met criteria for mild vitamin D depletion (i.e. 25(OH)D < 75 nmol/l; OM=13, VG=7, VN=5). Eleven children with elevated B12 levels (i.e. hlotranscobalamin >169,2 pmol/l; VG=3, VN=8) were identified. On the contrary, vitamin B12 deficiency was not present in this children group.

In adults (Table 4) we found comparable anthropometric characteristics in all three groups. Vegans had lower diastolic blood pressure compared to the other groups. In omnivores, we observed significantly higher levels of total and LDL cholesterol than in both plant-based diet-adhering groups. We did not find any clear differences in serum concentrations of biogenic elements except for zinc, which was significantly different among all three groups with vegans being the lowest one (VN<VG<OM). Iron metabolism parameters were comparable among groups excluding the serum ferritin which was lower in both VN and VG when compared to OM. While vegans had significantly higher serum PTH and vitamin D concentrations and lower urea and creatinine concentrations when compared to the other groups, these values nevertheless fell in the normal reference range. Active B12 showed a trend towards significant differences in vegans having the highest levels while vegetarians having the lowest levels. Correspondingly, serum MMA values were highest in vegetarians and lowest in vegans. Folate levels were lowest in omnivores. Nearly all statistically significant differences among the groups identified remained significant after adjustment for non-dietary variables. Of note, we identified subjects with values out of the reference range: twelve subjects had low zinc (i.e., zinc < 9,8 mmol/l; OM=2, VG=1, VN=9), sixty-five subjects low iron stores (i.e. ferritin < 22 (μg/l); OM=12, VG=20, VN=33) and seventy-six low vitamin D status (i.e. 25(OH)D <75 nmol/l; OM=35, VG=25, VN=44). One vegan participant had vitamin B12 deficiency (i.e. vitamin B12 <27,4 pmol/l). On the contrary, we identified subjects above the upper reference limit in active vitamin B12 (OM=2, VG=1, VN=2), total and LDL cholesterol (VN=15, VG=13, OM=17), and PTH (VN=6).

Evaluation of nutritional risks in marginal subgroups

Potential nutritional deficiencies may not manifest in the majority population, but marginalized groups could still be at risk. Therefore, we used quantile regression models to analyze not only the central tendency (median values) but also the 20th and 80th percentiles of each clinical outcome.

In children, diet effects were mostly consistent across quantiles (see Supplementary Tables 2-3). An exception was found in urinary iodine levels, which significantly differed between VN and OM at the 20th percentile but not at another percentile. The adjusted VN-OM difference at the 80th percentile was also less than half compared to differences at the 20th and 50th percentiles in both age groups of children (see Suppl. Tables 2 and 3). Interestingly, vegan children included the 6 lowest but also the 3 highest urinary iodine levels. Altogether, it suggests that while urinary iodine levels may generally be lower in vegans compared to omnivores, vegan diets may be associated with wider spread towards higher iodine levels, hiding mean and median difference between diets.

In adults, the most interesting results were found for serum selenium, where the medians and 80th percentiles were similar across diet groups, but a significant and relatively large difference between VN vs OM in the 20th percentile (-0.17 µmol/l, p = 0.039) was found. VG vs OM showed a similar but opposing trend (-0.19 for the 20th percentile, but diff. -0.05 with p=0.01 for the median and the 80th percentile respectively). Moreover, although unadjusted serum selenium differences were modest between omnivores and plant-based diet (0.03 µmol/l), there was a larger unadjusted difference from OM levels lower quartiles (0.17 and 0.25 µmol/l in VN and VG respectively). This suggests that while serum selenium levels may not be universally higher in omnivores, part of vegans may have inadequate selenium stores.

For other characteristics, the most differences between diet groups were generally similar across percentiles (Supplementary Table 4). In some variables, there were significant differences between OM and VN (BMI, fat-free mass, serum zinc, and serum creatinine). However, the effects of diets were similar in these variables across quantiles, with differences in statistical significance potentially reflecting random variation or distributional characteristics, such as heavier tails for larger values causing inconsistent estimation of 80th percentiles.

Family clustering and covariates' importance

Besides diet, we anticipated that clinical characteristics are influenced by other factors, namely those clustering within families. rLME allows us to assess the relative importance of covariates and quantify the extent to which these characteristics cluster within families. To evaluate the importance of each variable in a model, we employed the Akaike Information Criterion (AIC), which estimates a variable's contribution to the model’s out-of-sample predictive accuracy. A larger decrease in AIC following the inclusion of a variable indicates a larger contribution to the model’s predictive capability. The results are summarized in Figure 2. When the AIC showed the large importance of family, we also reported an adjusted intraclass correlation coefficient (ICC), indicating how much of the total variability in the outcome is due to the grouping structure, i.e. family, after accounting for other variables.

In young children, age was the most important covariate for Ca, P, Fe, transferrin saturation, CTx, IGF-1, and particularly P1NP. Sex had negligible effects. Breastfeeding was the most important factor for ferritin levels, but also contributed to P1NP and IGF1. Diet was the most crucial for MCV, MMA, and folate. Anthropometric factors, mainly weight and height, were most importantly shaped by the birth weight. Surprisingly we did not find an effect of supplementation on relevant minerals or vitamins.

In children >3 years, age was crucial for HDL, creatinine, homocysteine, and IGF1. Sex had again negligible importance except for a modest contribution to calcium levels. Breastfeeding-related covariates had negligible importance, contrasting the situation in younger children. Birth weight shaped the actual weight and height. Iron supplementation was the strongest predictor for transferrin levels. Vitamin B12 supplementation was related to B12 levels and the diet was the most important factor for magnesium, MCV, and folate levels.

As family importance could be best inferred from larger data, with more observations per family, we decided to infer the importance of family clustering from the joint analysis of all children (merging both age groups together). Clustering within the family was found for most variables, but the most prominent was this factor for height (ICC = 57%), HDL cholesterol (59%), B12 (60%), PTH (59%), uric acid (56%), and particularly vitamin D (67%).

In adults, age was the main determinant of CTx but had little to no importance for other variables. In contrast to children, most variables were shaped by sex. In addition to anthropometrics, sex was crucial also for blood pressure, HDL, TG, Ca, and P levels, variables reflecting iron metabolism, hemoglobin, urea, and creatinine, uric acid, and homocystein. We did not identify the importance of vitamins and mineral supplementations. Diet was the main determinant of total and LDL cholesterol, PTH, but contributed to other variables as well. Family clustering was substantial for circulating selenium (ICC = 73%), zinc (41%), urinary iodine (58%), B12 (44%), and folate (35%), altogether likely reflecting family-specific diet habits.

Dietary intake

Dietary intake of the main macro- and micronutrients of interest is summarized in Figure 4 and Suppl. Table 5-7.

The differences among groups were negligible in children < 3 years old. In this age group, the diet composition was similar across all groups, only VN and VG children had significantly lower intake of saturated fats and cholesterol. We identified a tendency towards lower intake of selenium (p=0.057) and higher intake of fiber (p=0.074) in both groups preferring plant-based diets compared with the OM group.

Similarly, in the age stratum of preschool children (children > 3 years old), the total energy, carbohydrate, and fat intake was not different among the groups. Both groups adhering to plant-based diets (VN and VG) had a significantly higher intake of fiber and consumed less cholesterol (VN<<VG) compared with the OM group. The protein intake tended to be lower in VN and VG compared with OM but it reached statistical significance only in the VN group. Micronutrient intake was comparable among all groups except selenium, which intake was lower in both VN and VG.

Among adults, all groups had comparable total energy, sugar, protein, and fat intake. As expected, VN participants had a lower intake of saturated fats and cholesterol (VN < VG < OM) and a higher intake of fiber (VN > VG > OM) compared with the OM group. Carbohydrate intake was higher in VN only. Concerning micronutrients, the VN group had a higher intake of magnesium, zinc, and iron than both the VG and OM groups. Participants adhering to plant-based diets (VN and VG) had a lower intake of iodine and selenium than OM.

Supplementation habits

Micronutrients were supplemented by many study participants in the form of dietary supplements, but the exact dose is generally very complicated to quantify. The diet record may not reflect year-round supplementation and may underreport overall intake in irregularly supplementing persons. Therefore, we used a qualitative approach in surveying individual nutrient use among the study participants. The results are summarized in Suppl. Table 8. The groups differed significantly in supplementation habits, namely in the intake of B12; vitamin D, and n-3 fatty acids. A high proportion of vegans and vegetarians supplemented vitamin B12 across all age strata; omnivores did not supplement B12 at all. Vegans and vegetarians also supplemented n-3 fatty acids (VN>VG). For all groups, there was a significant proportion of individuals who supplemented vitamin D; although the number of supplementing omnivores is about half that of vegans or vegetarians.

Clinical variables as diet predictors

To explore how dietary patterns influence clinical characteristics, we employed elastic net logistic regression to determine whether the clinical characteristics could effectively discriminate between different diet groups. This approach provides insight into the extent to which diet shapes health outcomes, offering a predictive perspective on the role of diet in determining clinical profiles.

For each group, i.e. age-specific subgroups within children and adults, we began by fitting a baseline model incorporating basic subject characteristics (age, sex, and, for children, breastfeeding status) as predictors. Subsequently, we expanded our analysis with a more complex model that included also diverse clinical outcomes as predictors. The predictive capacity of clinical variables was estimated as the difference between the discriminative capacity of complex and baseline models, expressed as a difference between out-of-sample areas under ROC curves of both models (AUC_gain) (Table 5).

Generally, we were able to reliably discriminate between VN and OM in adults, with out-of-sample AUC 0.82 (95% CI: 0.69 to 0.92), whereas it was only 0.54 in the baseline model (not utilizing clinical characteristics), with a mean (?) AUC gain of 0.28 (0.08 to 0.49). The strongest predictors of VN diet are lower glycemia, total cholesterol, zinc, ferritin, and urea, and higher P1NP and folate.

In both children's age groups, the predictions were also relatively stronger when discriminating between VN and OM. Here the complete model performance was 0.74 (95% CI: 0.54 to 0.91) and 0.75 (95% CI: 0.45 to 0.97), respectively. However, these prediction models were shown unstable during bootstrap validation, providing more inconsistent performances over bootstrap resamples. The main predictors in these models were variables associated with vitamin supplementation, i.e. the strongest predictor for VN diet in children < 3 years old was low MMA serum concentration. When we omitted these variables (B12, homocystein, MMA, folate, vitamin D) from the set predictors, the performance of the complete models substantially decreased, i.e. AUC_children<3yr=0.61 (95% CI: 0.37 to 0.82), AUC_children>3yr=0.69 ((95% CI: 0.42;0.94) (Suppl. Table 10).

This cross-sectional study into dietary intake analyzed nutritional status, and clinical outcomes among vegan, vegetarian, and omnivore families. The major findings of the study are:

1/ No significant differences in anthropometric and growth characteristics observed in children among dietary groups;

2/ Comparable anthropometric characteristics in adults observed among dietary groups, but vegans had lower diastolic blood pressure, total and LDL cholesterol, urea, and creatinine levels;

3/ Lower serum concentrations of total and LDL cholesterol, and higher vitamin D levels in vegan children > 3 years old;

4/ Lower urinary iodine and higher folate, vitamin B12, and vitamin D levels in vegan children < 3 years old;

5/ On the assessment of covariate effects, dietary groups significantly impacted cardiovascular, iron metabolism, nutrition markers, and serum PTH concentration in adults. Family impacted height, micronutrient status (Se, Zn, urinary iodine), and vitamin levels (folate, B12, and D);

6/ With the exception of adults, we were unable to reliably discriminate between dietary groups, based on clinical and anthropometric characteristics.

Vegan children’s growth and anthropometric characteristics are comparable to omnivores

Although certain sources suggest that a well-planned vegan diet when properly supplemented with specific nutrients could be nutritionally adequate to support growth and development in vegan children^15–17, the evidence regarding the growth and development of vegan or vegetarian children who follow the respective diets from birth is still inconclusive. There are signals that vegan children exhibit a lower BMI⁸ and height⁹ compared to their omnivorous counterparts. Lower caloric density and higher fiber content of plant-based diets, which often lead to lower net energy intake, were among the plausible explanations¹⁸. While higher fiber intake is generally considered a major benefit of plant-based diets^5,16,18, in children, a high-fiber diet may cause increased satiety and lead to inadequate total energy and protein intake, due to their smaller stomach volume. In addition, excess fiber may interfere with the absorption of fats and minerals and is also associated with higher intakes of antinutritional substances that impair the absorption of some already critical nutrients in a plant-based diet¹⁹. In the present study population, we found no difference in total caloric intake across age strata, though we confirmed that vegans and vegetarians generally had higher intakes of fiber.

Despite some differences in nutrient composition of the diet, we found no difference in the growth and anthropometric characteristics among children. These findings corroborate Finnish study outcomes on preschool vegan children (median age 3.5 years)²⁰ but contrast the results of the Polish study performed on children aged 5–10 years⁹ where vegan children were found to be shorter albeit they did not differ in other measured characteristics. On multivariate analyses, we showed that the primary predictor of height in our study was family background rather than dietary group. This finding may account for the discrepancies in the previously published results. Interestingly, unadjusted comparison in the children > 3 years old suggested possibly lower IGF-1 whereas adjusted analyses (rLME and QR) did not, but suggested that IGF-1 is rather shaped by family and age instead.

Altogether, we found no indices of growth challenge in vegan and vegetarian children, who followed the respective diet from birth, but an important effect of a family.

Indicators of better cardiometabolic health in vegans can be identified as early as preschool age

Cardiometabolic diseases are among the leading global health concerns with diet being a significant environmental factor contributing to their rise^21–24. Large cross-sectional and prospective cohort studies in Western countries such as Adventist Health studies, EPIC-Oxford, and UK Women´s Cohort, which included a significant proportion of vegan and vegetarian adults following plant-based diets exhibit a lower prevalence of obesity and a reduced risk of ischemic heart disease and type 2 diabetes (T2D) compared to omnivores from similar backgrounds²⁵. These findings were associated with lower LDL cholesterol, BMI, and blood pressure.

The long-term beneficial effects of a vegan diet have inspired its use for patients with metabolic syndrome and T2D. According to a meta-analysis of 11 short-term intervention trials a healthy vegan diet led to better results considering cardiometabolic parameters (BMI, total and LDL cholesterol, glycated hemoglobin) compared with a healthy standard diet²⁶.

With the rising incidence of obesity across all age groups, the accumulation of cardiometabolic risk factors is shifting to younger populations and the COVID-19 pandemic has even accelerated this trend²⁷. Since cardiovascular events in children are rare the merit of evaluating cardiovascular risk factors in childhood has been a matter of debate²⁸. Only recently, the results of a large pediatric cohort study to evaluate cardiovascular risk, the International Childhood Cardiovascular Cohort (i3C) Consortium, demonstrated strong associations of childhood risk factors with major cardiovascular events in midlife²⁹. From this perspective, a reduction in risk-factor levels as early as during childhood may have the potential to lower the incidence of premature cardiovascular disease.

We found a clear trend towards increased CVD risk factors across age strata (infants < pre-schollers < adults) and dietary groups cholesterol (VN < VG < OM). While indices of cardiometabolic health in children <3 years old were comparable among groups, the omnivore pre-schoolers already exhibited higher levels of total and LDL cholesterol compared with vegans of the same age. The difference was then even more pronounced in adults. Moreover, adult omnivores had higher median BMI related to higher amounts of fat mass and higher DBP.

Our data suggest that adopting well-balanced plant-based diets as early as childhood could be among the counter-measures to the trend of increased CVD risk and premature death in adulthood.

Signs of higher bone turnover in vegans are related to impaired calcium homeostasis

There are indices that the bone health of vegans may be impaired, based on both cross-sectional^30,31, and prospective studies¹¹. Deficiencies in critical nutrients for bone formation and mineralization such, as calcium and vitamin D, often precede a decline in bone density and fractures ^11,30,32,33. However, bone health may also be influenced by other nutrients such as zinc, vitamin B12, and omega-3 fatty acids that are less abundant or absent in plant-based diets compared to omnivorous diets^32,33.

Lower bone mineral density was shown in both adults and children⁹ vegan groups. However, it has been debated that the lower BMD reflects lower BMI in vegans³⁴. Nevertheless, results from a large EPIC-Oxford cohort study indicated that these risks eventually led to a higher incidence of all-site fractures in vegans. Accumulating evidence was reviewed in a meta-analysis, showing both vegetarians and vegans have higher risks of fractures³².

We observed a trend toward higher bone turnover in vegans as documented by higher P1NP in adults and preschool children and PTH levels slightly but significantly higher in adults. Paradoxically, vitamin D levels were generally higher in vegans across all age strata. These results could be attributed to regular supplementation of vitamin D in vegans while having a lower intake of calcium³⁵. Calcium intake was lower in vegans across age strata though statistical significance was reached only in adults. Moreover, plant sources of calcium have not only lower content but also lower bioavailability, further decreasing net calcium gain from the diet³⁶. Anyhow, similar findings of high P1NP and comparable CTx were already reported in this group.³⁷ Despite the potentially lower calcium intake and signs of increased bone turnover among vegans, we have not observed a higher reported incidence of any bone fractures or fractures most commonly associated with osteoporosis (spine, hip, distal radius, and proximal humerus) in any vegan age strata.

The interpretation of these results is limited by two factors. Firstly, we did not analyze urinary calcium losses so we cannot confirm the hypothesis of low calcium intake and higher bone turnover. Secondly, we have not assessed bone quality so we cannot conclude on any decline in bone mass.

In conclusion, despite adequate vitamin D supplementation, signs of impaired calcium metabolism were observed in vegans, although these were not linked to a higher incidence of reported fractures. These findings raise potential safety concerns regarding calcium intake in vegan diets and highlight the need for optimizing calcium and vitamin D supplementation and/or the intake of fortified foods. Further research is necessary to determine whether these risks affect future fracture risk.

Vegans are at risk of iodine deficiency

Iodine is a critical nutrient for plant-based dietary regimens. The major sources of dietary iodine are milk, dairy products, eggs, fish, and seafood³⁸. Vegan diets generally contain lower levels of iodine because the iodine content of plant sources is lower^39,40. Seaweeds on the other hand can be a possible source of excess iodine, but their iodine content is highly variable⁴¹.

In the Czech Republic, salt is routinely fortified with iodine, which explains that compared to other European countries iodine status in Czech school children and adults is sufficient (i.e. median standardized UIC >100 µg/L)⁴². However, in the groups that lack major dietary sources of iodine, the deficiency may be an important safety concern. We and others have already shown, that vegan children^5,12 as well as adults⁴³, are more likely to have lower iodine status.

In the current study, we found that among children groups the iodine intakes were not significantly lower in vegans, though a trend towards lower intakes in vegans were observed. Adult vegans had significantly lower iodine intake compared to other dietary groups. Lower iodine intake among vegans translated to lower urinary iodine concentrations in vegans and vegetarians across all age strata, mostly expressed in the group of children < 3 years old with the highest prevalence of iodine deficiency (i.e. UIC <100 µg/L) in vegan children. But, in line with previous findings^12,30, all these children had TSH in the normal range.

Of note, concerns about excessive iodine intake from seaweeds that may be popular in the vegan population, were raised.⁴¹ But we have not identified any vegan subject with excessive iodine intake in the current study.

As children are particularly vulnerable to iodine deficiency, which can impact psychomotor development, clear guidelines are essential for supplementing iodine in plant-based diets across all age groups. Adequate iodine intake among vegans is warranted to mitigate potential health risks associated with deficiency.

Vegans have lower iron stores unrelated to hemoglobin levels

Previous studies indicated that a vegan diet provides sufficient iron intake primarily sourced from plant-based foods in the form of non-heme iron ^44,45. However, non-heme iron absorption in vegan diets is susceptible to various inhibitors such as phytates, polyphenols, and calcium, which collectively contribute to reduced iron absorption efficiency in vegan diets, despite adequate iron intake from plant sources.⁴⁶ Some studies on vegans have shown that low iron status did not correlate with lower iron intake levels.^47–49

In line with the evidence, we found comparable iron intake among children’s groups and even higher iron intake in vegan adults, contrasting with significantly lower ferritin levels in vegan adults. Despite the lower ferritin levels in adult vegans, comparable hemoglobin levels were found among groups across age strata, though in the whole sample, these were positively related (R=0.4, p<0,0001). Even these findings correspond with existing evidence⁴⁷.

In line with previous research^9,50, we foundsignificantly higher corpuscular volumes of erythrocytes (MCV) in VN and VG with a clear trend of VN>VG>OM. Though it is well established that B12 deficiency relates to higher MCV⁴⁸and low ferritin levels to lower MCV,we observed an opposite trend. Whether there is another diet-related mechanism beyond iron and B12 contributing to MCV is to be further studied. Anyhow the differences were subtle and very likely below clinical relevance.

Family may be a stronger predictor of some nutrient status indices than dietary group

Families share dietary patterns, influencing the nutritional choices that impact all household members, especially children during critical stages of growth and development¹³. We confirmed that a covariate family impacted differences found among groups and across ages. In children > 3 years, family covariate improved the predictability of anthropometric measures. Growth restriction is among the concerns in children eating plant-based. We showed that both height and IGF-1 related to the family covariate, underlining the importance of assessing parental height and shared risks within families when analyzing the growth of a child. Besides we found that serum concentrations of PTH, vitamin D, and urinary iodine were also related to the family covariate. In adults, the covariate family affected the predictability of height, some micronutrient concentrations (serum Se, Zn, and urinary iodine), some vitamins (folate, B12, and D), and the incidence of allergy.

The differences in vitamin and micronutrient levels could be explained by supplementation habits as both the portion of supplementing families and circulating biomarkers of these elements were higher in VN and VG groups respectively.

Data on associations of nutrient status and family risks are scarce given the complexity of the research design, so further research is needed to replicate the results at a larger scale. Nevertheless, we conclude that family may be an important determinant of the nutritional status and dietary interventions should target the families and focus on parental education.

Strengths and limitations

In the current work, a baseline comparison of the recently initiated prospective cohort study, we enrolled 95 families with shared distinct eating habits with children being on the respective diet from birth. While there is ample evidence on the risks and benefits of vegan diets among adults, children remain currently an under-represented population in nutritional research on the effects of plant-based diets. Dietary habits established in childhood can significantly shape health outcomes later in life, underscoring the importance of early dietary choices^13,14. Therefore, focusing on this research group is an unmet need as has been repeatedly discussed in the literature⁵¹.

The unique family-based design allowed us not only to study the individual outcomes based on group allocation but also the family factor, as an independent predictor. Indeed, it has been shown that the shared household represents an important determinant not only of health status but also of dietary intake⁵².

Some limitations need to be listed. Cross-sectional design does not allow for inferring causality and may be biased by several confounders. We tried to overcome this by using multivariate analysis and quantile regression to capture both potential confounders and differences beyond central distribution tendencies. Among other confounders of the link between diet and health outcomes are sociodemographic status, education level, and physical activity which were not taken into consideration in the study design.

There are errors inherent to the analysis of the dietary intake. We relied on 3 days prospective weighted record, which is only a snapshot of average intakes over the year. Moreover, supplementation could be completely missed by individual records. We tried to overcome these by random enrolment across seasons, and by qualitative assessment of the supplementation.

Lastly, a selection bias based on willingness to participate in such a study needs to be pointed out. Our participants are namely well-educated and motivated families coming mainly from the capital city urban area.

To conclude, we described the differences among families with distinct eating habits in anthropometric measures, health, and nutritional status indices. While vegan diets offer potential health benefits, including reduced cardiometabolic risks, addressing specific nutritional challenges such as iodine deficiency and optimizing calcium intake remains crucial. Future studies should focus on replicating these findings on a larger scale and exploring comprehensive public health strategies to support optimal health across different stages of life of an individual and the family.

Ethics approval and consent to participate

The study was reviewed and approved by the relevant Institutional Review Board of University Hospital Kralovske Vinohrady 22/06/2020 under no. EK-VP/39/0/2020. Written consent was signed before the enrolment to the protocol for each study participant. For children, parental consent was sought. The study was performed under the guidance of the Helsinki Declaration.

Design and the study population

An overview of the study design is depicted in Figure 1. The details of the study protocol are available online (Selinger et al., 2024) and in the Suppl. Methods. In brief, families consisting of two adults and at least one child under 7 years of age with the same dietary eating pattern (self-identified vegan, vegetarian, or omnivore) were enrolled (period 10/2021-10/2022). The exclusion criteria consisted of a different diet in individual family members, any disease associated with malabsorption, and inability to undergo full clinical examination and biospecimen sampling. Each subject underwent a clinical visit with an anthropometric examination, detailed medical history, and venous blood sampling after 12 hours. All laboratory parameters were analysed in ISO-certified laboratories. Analytic methods and analysers are summarised in Suppl. Table 9.

Nutritional assessment

A 3-day (two weekdays and one weekend day) weighted dietary record method was used to evaluate the dietary intake. Nutrient and energy intake data were analysed using the NUTRIXo nutritional software, based on validated FCDBs (ArcaiSoft, Czech Republic). For products not listed in any of the databases, the dietitian recorded nutrient content from the product packaging.

Statistical analyses

All statistical analyses were conducted using R, version 4.4.0 (2024-04-24) (R Core Team 2023). The overall difference among groups was evaluated using either the Kruskal-Wallis test (numerical variables) or the Chi-squared or Fisher test (categorical variables). Differences between the pair of groups were evaluated using the Mann-Whitney U test (numerical variables) or as an overall test but with a subset of individuals (categorical variables). Quantile regression was employed to assess differences in continuous clinical outcomes between diet groups, modelling the median, 20th, and 80th percentiles of each variable. Linear mixed-effect models were used to assess the random effect of family. To assess the predictive power of clinical outcomes on a diet strategy, we employed Elastic Net logistic regression. The statistical methods are described in detail in Supplementary Materials.

25(OH)D, 25-hydroxyvitamin D

BMI, Body mass index

C-HDL, HDL cholesterol

C-LDL, LDL cholesterol

Ca, Calcium

Cr, Creatinine

CTx, Beta Cross Laps

DBP, Diastolic blood pressure

FCDB, Food composition database

FPG, Fasting plasma glucose

GRP, group

Hb, Haemoglobin

HoloTC, Holotranscobalamin (active B12)

IGF-1, Insulin-like growth factor

MCV, Mean corpuscular volume

Mg, Magnesium

MMA, Methylmalonic acid

OM, Omnivore

P, Phosphate

P1NP, Procollagen type I aminoterminal propeptide

PTH, Parathyroid hormone

SBP, Systolic blood pressure

Se, Selenium

sTfR Index, Soluble transferrin receptor/log Ferritin Index

STFR, Soluble transferrin receptor

TC, Total cholesterol

TG, Triglycerides

TIBC, Total iron-binding capacity

UA, Uric acid

UIC, Urine Iodine Concentration

VN, Vegan

VG, Vegetarian

Zn, Zinc

Data and code availability

Data, code, and detailed statistical methodology description are available in an online GitHub repository: https://github.com/filip-tichanek/kompas_clinical.

Acknowledgments

The authors would like to express deep gratitude to the study participants whose contributions have made it possible to advance knowledge in the field.

Funding

The study was supported by the Ministry of Health, Czech Rep., no. NU21-09-00362, the project LX22NPO5104, Funded by the European Union—Next Generation EU, and the Charles University grant support 260646/SVV/2023.

Authors’ contributions

JG, MC, TK, EE, and PD were involved in the conceptualization, methodology, and data curation. FT, PP, TK, and MC prepared the analytical plan and performed all statistical calculations. MH, AO, JG, MS, ES, JP and DH conducted the clinical examinations. MH, AO, JG, and MC drafted the manuscript. All authors were involved in writing, reviewing, and editing the final version of the manuscript.

Competing interests:

The authors declare no competing interests related to the study.

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Table 1 Groups’ characteristics.

		Vegan	Vegetarian	Omnivore
n	Adults	92	45	50
	Children < 3 yo	40	15	22
	Children > 3yo	21	22	22
Age (years)	Adults	33.9 (31.5; 37.2)	36.7 (33.4; 38.2)	35.8 (32.8; 40.0)
	Children < 3 yo	1.4 (0.9; 2.1)	1.5 (0.7; 2.0)	1.5 (1.0; 2.2)
	Children > 3yo	4.7 (3.6; 5.8)	5.3 (4.5; 6.8)	5.5 (4.3; 6.3)
Sex F/M	Adults	47/45	23/22	25/25
	Children < 3 yo	21/19	9/6	11/11
	Children > 3yo	10/11	9/13	13/9
BreastFeed_full (month)	Children < 3 yo	6.0 (5.0; 6.0)	6.0 (5.8; 6.0)	6.0 (6.0; 6.0)
BreastFeed_full (month)	Children > 3yo	6.0 (6.0; 6.0)	7.0 (6.0; 7.8)	6.0 (5.3; 6.0)
BreastFeed_total (month)	Children < 3 yo	12 (8; 17)	18 (9; 21)	10 (9; 14)
BreastFeed_total (month)	Children > 3yo	18 (13; 30)	18 (16; 22)	10 (8; 14)
Birth weight (g)	Children < 3 yo	3,425 (2,919; 3,705)	3,325 (2,993; 3,645)	3,290 (3,019; 3,575)
Birth weight (g)	Children > 3yo	3,500 (3,060; 3,650)	3,400 (3,220; 3,555)	3,430 (3,088; 3,743)
Allergies	Adults	12 (13%)	12 (27%)	17 (34%)
	Children < 3 yo	1 (2.5%)	1 (6.7%)	5 (23%)
	Children > 3yo	2 (9.5%)	4 (18%)	4 (18%)
Fractures	Adults	36 (40%)	23 (51%)	27 (54%)
	Children < 3 yo	0	0	1
	Children > 3yo	1 (4.2%)	0	3 (13%)

Table 2 Clinical variables in children <3 years old among dietary groups.

	Vegan	Vegetarian	Omnivore	K-W test	VN vs OM	VG vs OM	VN vs VG
Anthropometrics
Body weight (percentile)	34 (14; 68)	48 (30; 71)	63 (34; 78)	0.2
Body height (percentile)	38 (15; 64)	51 (31; 68)	43 (33; 74)	0.3
BMI (percentile)	56 (35; 70)	56 (42; 73)	55 (45; 78)	0.7
Weight to height ratio (percentile)	53 (40; 66)	61 (41; 70)	58 (46; 77)	0.5
Glucose metabolism
FPG (mmol/l)	4.4 (4.2; 4.6)	4.7 (4.3; 4.9)	4.5 (4.3; 4.6)	0.4
Lipid metabolism
TC (mmol/l)	3.9 (3.5; 4.0)	3.9 (3.4; 4.3)	4.0 (3.1; 4.4)	0.8
C-HDL (mmol/)	1.3 (1.0; 1.4)	1.1 (1.0; 1.3)	1.1 (0.8; 1.4)	0.7
C-LDL (mmol/l)	2.0 (1.7; 2.2)	2.3 (1.9; 2.6)	2.0 (1.5; 2.8)	0.2
TG (mmol/l)	1.1 (0.8; 1.5)	0.8 (0.7; 1.2)	1.1 (0.9; 1.5)	0.4
Biogenic elements (serum)
Calcium (mmol/l)	2.63 (2.55; 2.68)	2.64 (2.60; 2.72)	2.65 (2.54; 2.72)	0.8
Phosphorus (mmol/l)	1.72 (1.63; 1.83)	1.79 (1.69; 1.83)	1.70 (1.66; 1.80)	0.7
Magnesium (mmol/l)	0.88 (0.84; 0.92)	0.89 (0.85; 0.97)	0.87 (0.82; 0.90)	0.4
Selenium (mmol/l)	0.75 (0.54; 0.94)	0.73 (0.64; 0.86)	0.75 (0.68; 0.95)	>0.9
Zinc (mmol/l)	10.65 (9.80; 11.75)	10.80 (10.48; 11.20)	11.70 (10.70; 13.40)	0.2
Iron (μmol/l)	11.1 (8.1; 16.8)	10.2 (5.4; 12.5)	9.6 (8.3; 14.6)	0.4
Iron metabolism
TIBC (μmol/l)	71 (66; 77)	73 (69; 75)	75 (72; 79)	0.3
Ferritin (μg/l)	12 (9; 16)	11 (9; 14)	15 (9; 21)	0.4
Transferrin (g/l)	2.8 (2.6; 3.0)	2.9 (2.7; 3.0)	3.0 (2.9; 3.2)	0.3
Transferrin saturation (%)	16 (11; 24)	14 (7; 18)	13 (12; 18)	0.4
sTfR Index	1.5 (1.3; 1.9)	1.6 (1.4; 1.8)	1.6 (1.3; 2.1)	>0.9
STFR (mg/l)	1.7 (1.5; 2.0)	1.65 (1.4; 1.9)	1.8 (1.7; 1.9)	0.4
Hemoglobin (g/l)	119 (115; 121)	117 (110; 121)	116 (112; 123)	0.7
MCV (fl)	79.7 (76.8; 80.8)	77.6 (75.8; 80.7)	76.8 (70.9; 78.8)	0.021	0.005/ 0.002*	0.239	0.369
Bone health
PTH (pmol/l)	3.2 (2.1; 4.0)	2.6 (1.8; 3.2)	2.5 (1.4; 3.8)	0.2
CTx (ug/l)	1.2 (1.1; 1.4)	1.3 (0.9; 1.4)	1.2 (1.0; 1.3)	0.4
P1NP (ug/l)	988 (670; 1,201)	986 (861; 1,201)	920 (585; 1,201)	0.8
Others
UIC (μg/l)	98 (66; 168)	190 (134; 271)	169 (135; 231)	0.036	0.025	0.744	0.060
Urea (mmol/l)	3.6 (2.7; 4.4)	3.2 (2.7; 3.9)	4.3 (3.3; 5.6)	0.1
Creatinine (μmol/l)	19 (18; 23)	20 (19; 23)	21 (19; 23)	0.4
Uric acid (μmol/l)	218 (187; 255)	223 (175; 241)	222 (191; 267)	0.8
Active B12 (pmol/l)	138 (91; 184)	59 (46; 85)	84 (72; 93)	<0.001	0.001	0.152	0.002/ 0.021*
Homocysteine (μmol/l)	7.1 (6.0; 8.9)	9.0 (7.2; 12.1)	9.3 (7.4; 12.0)	0.004	0.002/ 0.006*	0.866	0.043/ 0.009*
MMA (nmol/l)	152 (125; 187)	305 (190; 628)	280 (248; 386)	<0.001	<0.001/ <0.001*	0.879	<0.001/ 0.018*
Vitamin D (nmol/l)	104 (83; 118)	98 (81; 129)	82 (67; 98)	0.03	0.008/ 0.036*	0.110	>0.9
Folate (μg/l)	18.1 (15.7; 22.4)	18.2 (16.9; 21.1)	14.4 (12.7; 17.2)	0.014	0.009/ 0.002*	0.017/ 0.005*	>0.9
Growth factor
IGF-1 (ug/l)	64 (45; 89)	63 (51; 83)	70 (48; 107)	>0.9

Table 3 Clinical variables in children >3 years old among dietary groups.

	Vegan	Vegetarian	Omnivore	K-W test	VN vs OM	VG vs OM	VN vs VG
Anthropometrics
Body weight (percentile)	40 (27; 67)	62 (28; 65)	52 (29; 73)	0.6
Body height (percentile)	42 (11; 61)	57 (38; 74)	41 (19; 76)	0.4
BMI (percentile)	43 (21; 68)	46 (32; 58)	46 (35; 63)	0.9
Weight to height ratio (percentile)	43 (17; 70)	47 (32; 57)	47 (30; 63)	0.8
Glucose metabolism
FPG (mmol/l)	4.3 (4.2; 4.6)	4.3 (3.8; 4.7)	4.3 (4.0; 4.7)	>0.9
Lipid metabolism
TC (mmol/l)	3.4 (3.2; 3.9)	3.7 (3.2; 4.3)	4.1 (3.6; 4.3)	0.045	0.009	0.307	0.263
C-HDL (mmol/)	1.2 (1.1; 1.5)	1.3 (1.1; 1.4)	1.4 (1.1; 1.7)	0.7
C-LDL (mmol/l)	1.7 (1.6; 2.0)	2.1 (1.8; 2.5)	2.2 (1.8; 2.3)	0.029	0.017	>0.9	0.029
TG (mmol/l)	0.9 (0.7; 1.1)	0.6 (0.6; 0.9)	0.9 (0.7; 1.2)	0.2
Biogenic elements (serum)
Calcium (mmol/l)	2.49 (2.47; 2.52)	2.50 (2.45; 2.58)	2.50 (2.44; 2.55)	>0.9
Phosphorus (mmol/l)	1.58 (1.43; 1.67)	1.54 (1.44; 1.63)	1.65 (1.49; 1.77)	0.5
Magnesium (mmol/l)	0.86 (0.81; 0.89)	0.83 (0.80; 0.87)	0.82 (0.79; 0.85)	0.09
Selenium (mmol/l)	0.77 (0.59; 0.86)	0.68 (0.60; 0.91)	0.83 (0.73; 0.92)	0.4
Zinc (mmol/l)	10.6 (9.6; 13.1)	11.0 (10.0; 13.0)	11.8 (10.8; 13.1)	0.4
Iron (μmol/l)	17 (11; 19)	13 (12; 19)	15 (9; 18)	0.8
Iron metabolism
TIBC (μmol/l)	67 (62; 74)	73 (63; 77)	72 (66; 78)	0.2
Ferritin (μg/l)	16 (12; 22)	19 (15; 26)	22 (17; 29)	0.1
Transferrin (g/l)	2.7 (2.5; 3.0)	2.9 (2.5; 3.1)	2.9 (2.6; 3.1)	0.2
Transferrin saturation (%)	25 (17; 30)	20 (14; 27)	20 (15; 25)	0.6
sTfR Index	1.2 (1.1; 1.4)	1.3 (1.0; 1.4)	1.1 (1.0; 1.4)	0.3
STFR (mg/l)	1.5 (1.4; 1.7)	1.6 (1.4; 1.7)	1.5 (1.4; 1.6)	0.6
Hemoglobin (g/l)	124 (117; 128)	129 (123; 135)	127 (122; 132)	0.09
MCV (fl)	82.7 (80.1; 84.2)	78.7 (76.9; 80.3)	80.3 (78.5; 82.1)	0.002	0.032/ 0.012*	0.129	0.001/ 0.001*
Bone health
PTH (pmol/l)	2.7 (2.3; 3.5)	2.8 (1.9; 3.3)	2.6 (1.8; 3.1)	0.8
CTx (μg/l)	1.3 (1.1; 1.4)	1.2 (1.1; 1.4)	1.2 (0.9; 1.5)	>0.9
P1NP (μg/l)	572 (519; 644)	492 (442; 649)	454 (402; 509)	0.013	0.003	0.110	0.200
Others
UIC (μg/l)	110 (76; 158)	126 (85; 179)	173 (141; 255)	0.032	0.019/ 0.017*	0.041	0.600
Urea (mmol/l)	4.1 (3.9; 4.8)	4.7 (4.3; 5.8)	4.5 (4.1; 5.4)	0.083
Creatinine (μmol/l)	30 (27; 33)	31 (27; 36)	33 (28; 37)	0.2
Uric acid (μmol/l)	226 (202; 261)	236 (221; 249)	222 (209; 243)	0.6
Active B12 (pmol/l)	143 (102; 228)	95 (81; 135)	118 (97; 132)	0.07
Homocysteine (μmol/l)	7.7 (6.4; 8.5)	8.0 (7.0; 10.0)	8.5 (8.0; 9.9)	0.13
MMA (nmol/l)	155 (136; 202)	170 (144; 257)	204 (170; 236)	0.067
Vitamin D (nmol/l)	94 (77; 104)	85 (70; 97)	70 (59; 85)	0.035	0.022	0.047	0.369
Folate (μg/l)	17.2 (13.1; 21.0)	17.5 (15.0; 18.8)	11.1 (9.3; 13.7)	<0.001	<0.001/ <0.001*	<0.001/ <0.001*	0.840
Growth factor
IGF-1 (ug/l)	105 (82; 137)	154 (122; 182)	156 (107; 192)	0.065

Table 4 Clinical variables in adults among dietary groups.

	Vegan	Vegetarian	Omnivore	K-W test	VN vs OM	VG vs OM	VN vs VG
Anthropometrics
BMI (kg/m²)	22.6 (20.8; 25.5)	22.9 (21.5; 26.1)	24.5 (22.4; 26.6)	0.063
Waist-to-hip ratio	0.78 (0.74; 0.82)	0.78 (0.73; 0.85)	0.79 (0.75; 0.85)	0.4
Hand grip (kg)	40 (31; 52)	43 (32; 51)	38 (30; 54)	0.9
Body fat (%)	21 (16; 26)	23 (16; 29)	21 (18; 29)	0.4
Body fat (kg)	14 (10; 18)	17 (11; 23)	17 (12; 21)	0.078
Free Fat Mass (kg)	53 (45; 65)	56 (49; 64)	54 (48; 68)	0.4
Blood pressure
SBP (mmHg)	121 (113; 134)	121 (111; 134)	125 (116; 139)	0.2
DBP (mmHg)	75 (70; 80)	79 (73; 86)	79 (70; 83)	0.013	0.107	0.284	0.005/ 0.032*
Glucose metabolism
FPG (mmol/l)	4.5 (4.1; 4.7)	4.4 (4.2; 4.7)	4.5 (4.3; 4.8)	0.5
Lipid metabolism
TC (mmol/l)	4.2 (3.7; 4.7)	4.3 (3.8; 5.0)	4.9 (4.3; 5.4)	<0.001	<0.001/ <0.001*	0.005/ 0.021*	0.157
C-HDL (mmol/)	1.4 (1.2; 1.7)	1.4 (1.2; 1.7)	1.5 (1.2; 1.7)	0.6
C-LDL (mmol/l)	2.2 (1.9; 2.8)	2.4 (2.0; 3.1)	2.8 (2.4; 3.3)	<0.001	<0.001/ <0.001*	0.016/ 0.041*	0.113
TG (mmol/l)	0.7 (0.6; 1.0)	0.8 (0.7; 1.0)	0.9 (0.6; 1.1)	0.4
Biogenic elements (serum)
Calcium (mmol/l)	2.46 (2.42; 2.51)	2.45 (2.41; 2.50)	2.44 (2.38; 2.52)	0.7
Phosphorus (mmol/l)	1.08 (1.01; 1.21)	1.12 (0.97; 1.26)	1.12 (1.06; 1.21)	0.5
Magnesium (mmol/l)	0.81 (0.77; 0.85)	0.81 (0.78; 0.83)	0.79 (0.77; 0.87)	>0.9
Selenium (mmol/l)	0.97 (0.75; 1.18)	0.97(0.67; 1.18)	1.00 (0.92; 1.17)	0.4
Zinc (mmol/l)	12.1 (10.8; 13.7)	13.1 (11.7; 14.1)	14.1 (12.9; 15.6)	<0.001	<0.001/ <0.001*	0.006/ 0.041*	0.032
Iron (μmol/l)	19 (15; 25)	15 (12; 25)	20 (16; 24)	0.2
Iron metabolism
TIBC (μmol/l)	68 (65; 76)	70 (62; 77)	68 (62; 74)	0.6
Ferritin (μg/l)	26 (14; 35)	25 (16; 50)	38 (23; 82)	0.003	<0.001/ <0.001*	0.025/ 0.007*	0.389
Transferrin (g/l)	2.7 (2.6; 3.0)	2.7 (2.5; 3.1)	2.7 (2.5; 2.9)	0.6
Transferrin saturation (%)	29 (21; 36)	22 (17; 39)	28 (24; 36)	0.2
sTfR Index	0.8 (0.7; 1.0)	0.9 (0.6; 1.2)	0.7 (0.6; 0.9)	0.013	0.010/ 0.003*	0.013/ 0.006*	0.620
STFR (mg/l)	1.2 (1.0; 1.3)	1.3 (1.1; 1.5)	1.2 (1.1; 1.3)	0.2
Hemoglobin (g/l)	145 (136; 155)	144 (134; 154)	147 (137; 154)	0.8
MCV (fl)	89.7 (87.1; 91.7)	86.7 (85.1; 89.5)	87.6 (86.0; 89.8)	<0.001	0.019/ 0.015*	0.133	<0.001/ <0.001*
Bone health
PTH (pmol/l)	3.3 (2.7; 4.1)	3.0 (2.2; 3.4)	3.0 (2.4; 4.0)	0.036	0,064	0,546	0.014/ 0.025*
CTx (μg/l)	0.4 (0.3; 0.6)	0.4 (0.3; 0.5)	0.4 (0.3; 0.6)	0.2
P1NP (μg/l)	52 (40; 72)	43 (37; 61)	45 (33; 59)	0.062
Others
UIC (μg/l)	91 (46; 147)	94 (42; 170)	120 (81; 202)	0.087
Urea (mmol/l)	4.0 (3.2; 4.5)	4.4 (3.8; 5.1)	4.8 (4.0; 5.7)	<0.001	<0.001/ <0.001*	0.166	0.007/ 0.013*
Creatinine (μmol/l)	64 (56; 72)	66 (58; 78)	70 (59; 81)	0.041	0.009/ 0.003*	0.234	0.328
Uric acid (μmol/l)	300 (247; 347)	290 (246; 350)	306 (254; 363)	>0.9
Active B12 (pmol/l)	90 (66; 124)	75 (54; 106)	84 (71; 109)	0.1
Homocysteine (μmol/l)	14.2 (12.5; 17.1)	15.8 (13.0; 21.5)	15.2 (11.9; 18.0)	0.2
MMA (nmol/l)	170 (135; 214)	236 (161; 305)	194 (160; 233)	<0.001	0.024	0.050/ 0.036*	<0.001/ <0.001*
Vitamin D (nmol/l)	76 (65; 92)	73 (59; 88)	67 (52; 78)	0.001	<0.001/ 0.007*	0.072	0.122
Folate (μg/l)	12.6 (9.7; 16.1)	12.9 (10.1; 15.4)	9.2 (7.3; 11.6)	<0.001	<0.001/ <0.001*	<0.001/ <0.001*	0.803

Table 5 Accuracy metrics of predictive models based on clinical variables.

AUC

AUC gain

p_val

baseline model

complete model

children

< 3 years old

VN_OM

0.50

(0.28; 0.69)

0.74

(0.54; 0.91)

0.243

(-0.005; 0.473)

0.058

VG_OM

0.64

(0.36; 0.90)

0.53

(0.17; 0.83)

-0.109

(-0.470; 0.228)

0.443

VN_VG

0.55

(0.29; 0.84)

0.61

(0.31; 0.91)

0.060

(-0.304; 0.420)

0.685

children

> 3 years old

VN_OM

0.51

(0.21; 0.79)

0.75

(0.45; 0.97)

0.237

(-0.135; 0.571)

0.158

VG_OM

0.70

(0.36; 0.93)

0.62

(0.31; 0.92)

-0.073

(-0.441; 0.333)

0.661

VN_VG

0.56

(0.28; 0.81)

0.63

(0.31; 0.90)

0.066

(-0.283; 0.413)

0.665

adults

VN_OM

0.54

(0.38; 0.71)

0.82

(0.69; 0.92)

0.275

(0.078; 0.494)

0.006

VG_OM

0.47

(0.30; 0.59)

0.62

(0.43; 0.80)

0.150

(-0.058; 0.370)

0.158

VN_VG

0.55

(0.40; 0.73)

0.64

(0.46; 0.78)

0.096

(-0.141; 0.285)

0.381

There is NO Competing Interest.

SupplementaryMaterials.docx

Dietary intake, Nutritional status, and Health outcomes among Vegan, Vegetarian, and Omnivore families: Results from the Observational Study

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