# Identification of Genetic Polymorphisms Associated with Interindividual Variability of Vitamin A Concentration in Adipose Tissue of Healthy Male Adults

Mark Pretzel Zumaraga, Charles Desmarchelier, Beatrice Gleize, Marion

Nowicki, Djaffar Ould-Ali, Jean-François Landrier, Patrick Borel

► To cite this version:

Mark Pretzel Zumaraga, Charles Desmarchelier, Beatrice Gleize, Marion Nowicki, Djaffar Ould-Ali, et al.. Identification of Genetic Polymorphisms Associated with Interindividual Variability of Vitamin A Concentration in Adipose Tissue of Healthy Male Adults. Journal of Nutrition, 2024, 154, pp.3693 - 3703. 10.1016/j.tjnut.2024.10.035. hal-04875940

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Nutrient Requirements and Optimal Nutrition

# Identification of Genetic Polymorphisms Associated with Interindividual Variability of Vitamin A Concentration in Adipose Tissue of Healthy Male Adults

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Mark Pretzel Zumaraga<fnref n="1" />,²,†, Charles Desmarchelier<fnref n="1" />,³,†, Beatrice Gleize<fnref n="1" />,‡, Marion Nowicki<fnref n="1" />, Djaffar Ould-Ali⁴, Jean-François Landrier<fnref n="1" />, Patrick Borel<fnref n="1" />,*

## ABSTRACT

**Background:** Adipose tissue vitamin A (VA), that is, mainly retinol (RET) and its esters, comes from preformed VA and proVA carotenoids present in our food. Adipose tissue VA acts as hormonal cue maintaining essential aspects of adipocyte biology, which includes fat mobilization and catabolism, energy balance, and glucose homeostasis, and it is thus of particular interest to study its determinants, including genetic ones.

**Objectives:** This study aimed to identify genetic variations associated with adipose tissue VA concentration.

**Methods:** Forty-two healthy male adults received, in a randomized crossover design, 3 test meals. Periumbilical adipose tissue samples were collected on 6 occasions, that is, at fast and 8 h after consumption of each meal. RET concentration was measured in both plasma and the adipose tissue following saponification. Participants were genotyped using whole-genome microarrays. A total of 1305 single nucleotide polymorphism (SNPs) in or near 27 candidate genes were included for univariate analysis. Partial least squares (PLS) regression was carried out to find the best combination of SNPs associated with the interindividual variability in adipose tissue RET concentration.

**Results:** Adipose tissue RET concentration was not associated with plasma RET concentrations (r = -0.184, P = 0.28). Interindividual variability of adipose tissue RET concentration was high (coefficient of variation = 62%). Twenty-nine SNPs were significantly (P < 0.05) associated with adipose tissue RET concentration and a PLS regression model identified 16 SNPs as explanatory variables of this concentration. The SNPs were in or near peroxisome proliferator activated receptor gamma, retinoid X receptor alpha, signaling receptor and transporter of retinol, cluster of differentiation 36, free fatty acid receptor 4, aldehyde dehydrogenase 1 family member A1, monoglyceride lipase, diacylglycerol O-acyltransferase 2, and polycystic kidney disease 1-like 2.

**Conclusions:** A combination of 16 SNPs has been associated with the interindividual of adipose tissue VA concentration in humans. This trial was registered at clinicaltrials.gov as NCT02100774.

**Keywords:** retinol, nutrigenetics, single nucleotide polymorphisms, adipocyte

## Introduction

The term vitamin A (VA) describes a group of fat-soluble molecules exhibiting the biological activity of all-trans retinol (RET) [1]. As VA cannot be synthesized by humans, it must be

obtained from the diet, either as preformed VA (RET and its esters), found in animal foods, or as proVA carotenoids (mainly β-carotene, α-carotene, and β-cryptoxanthin), mostly found in vegetables and fruits [2]. VA plays key roles in vision, immune, and brain functions and tissue remodeling and metabolism (as

**Abbreviations:** ALDH1A1, aldehyde dehydrogenase 1 family member A1; ANOVA, analysis of variance; BCO1, β-carotene oxygenase 1; CI, confidence interval; FFAR4, free fatty acid receptor 4; LD, lipid droplet; MGLL, monoglyceride lipase; PKD1L2, polycystic kidney disease 1-like 2; PLS, partial least squares; PPARG, peroxisome proliferator activated receptor gamma; RBP4, retinol-binding protein 4; RET, retinol; RXRA, retinoid X receptor alpha; SNP, single nucleotide polymorphism; STRA6, signaling receptor and transporter of retinol; VA, vitamin A; VIP, variable importance in the projection.

<footnote n="1">¹ C2VN, Aix Marseille Univ, INRAE, INSERM, Marseille, France; ² Department of Science and Technology—Food and Nutrition Research Institute, Bicutan, Taguig City, Philippines; ³ Direction générale de la recherche et de l'innovation, Paris, France; ⁴ Plastic & Anesthetic Surgery Department, Clinique Internationale du Parc Monceau, Paris, France</footnote>

<footnote>* Corresponding author. E-mail address: patrick.borel@univ-amu.fr (P. Borel).</footnote>

<footnote>† MPZ and CD contributed equally to this work.</footnote>

<footnote>‡ Present address for BG: UMR 408 SQPOV, INRAE, Avignon Université, Avignon, France.</footnote>
reviewed in Chen et al. [3]). The pleiotropic effects of VA are mainly mediated by its metabolite, retinoic acid, which primarily acts as a hormonal cue within cells controlling the expression of >500 genes through its binding to 2 classes of the nuclear hormone receptors, the retinoic acid receptors and the retinoid X receptors [4]. Adipose tissue is considered a major storage site of VA, next to the liver [5], containing 15%-20% of total body VA [6,7]. It is assumed that its main source is RET bound to retinol-binding protein 4 (RBP4) in the circulation. VA can also come from proVA carotenoids incorporated in LDL [8,9]. Additionally, during the postprandial period, VA derived from retinyl esters and proVA carotenoids, present in chylomicrons, remnants, are taken up by adipose tissue [10]. The RET internalized by cells initially undergoes 2 oxidative steps, the first forming retinaldehyde and the second retinoic acid. In addition to its oxidation to retinoic acid, RET can be esterified and stored as retinyl esters in the adipose tissue. Although liver VA content is considered the best biomarker to assess VA status [11], adipose tissue VA concentrations have been suggested to constitute a potential biomarker of VA status [12]. Moreover, when the liver's ability to store VA is impaired (for example, nonalcoholic fatty liver disease, cirrhosis, hepatocellular carcinoma, and alcoholic liver disease), the adipose tissue can increase its ability to store it [13]. Thus, adipose tissue VA probably contributes to maintaining blood RET concentration [14,15]. Intracellularly, VA regulates adipose tissue biology by suppressing lipid accumulation and adipocyte hypertrophy [16-19]. Furthermore, there is growing evidence that VA plays a role in the development of obesity [20,21], and regulates systemic insulin action and glucose homeostasis [22,23]. In light of the increasing chronic metabolic diseases worldwide such as obesity and type 2 diabetes [24,25], it is particularly relevant, from a pathophysiological point of view, to characterize the determinants of adipose tissue VA accumulation and storage for the development of novel preventive and therapeutic strategies.

Adipose tissue VA concentration not only depends on an individual's VA dietary intake but also on the efficiency of all metabolic steps, allowing preformed VA and proVA carotenoids to be absorbed by the small intestine, converted to RET or its esters and transported to the adipose tissue. Adipose tissue VA concentration also depends on its local metabolism, notably its conversion into RET from proVA carotenoids, its storage, and its release into the circulation [19,26]. VA storage in adipose tissue is regulated by numerous proteins [10]. Some of these proteins are directly involved in VA uptake and metabolism, for example, signaling receptor and transporter of retinol (STRA6) [15], beta-carotene oxygenase 1 (BCO1), the cleavage enzyme of proVA carotenoids into retinal [27], which can then be converted into RET by retinol dehydrogenase, or with retinoid mobilization and transport, for examples, the retinol-binding protein, cellular retinol-binding protein, cellular retinoic acid-binding protein, fatty acid-binding proteins [28,29]. Other proteins play more indirect role, for example, monoglyceride lipase (MGLL), which hydrolyzes monoglycerides from the lipid droplets in which VA is stored [30]. The fact that several proteins are involved in these steps suggests that polymorphisms in the genes encoding for these proteins could partially modulate their expression or activity and, in turn, affect this concentration [19,31-33]. However, no data are available on the effects that such genetic variations could have on this phohormone concentrations in adipose tissue.

The aim of this study was therefore to identify genetic varia-
tions associated with adipose tissue RET concentration in a pop-
ulation of healthy participants. This objective initially led us to
better characterize the interindividual variability of adipose tis-
sue RET concentration in this population. To this end, we
measured RET concentration in subcutaneous adipose tissue
samples of 42 healthy male adults, and we assessed the associa-
tions between its interindividual variability and single nucleotide
polymorphisms (SNPs), following a candidate gene approach.

Methods

Participants

Adult male participants were recruited between January 2009 and July 2010, based on the following inclusion criteria: a BMI of ≤25 kg/m² (nonoverweight), nonsmokers, and no history of chronic diseases, hyperlipidemia, or hyperglycemia. Participants were excluded if they had taken any medications or dietary supplements known to affect lipid metabolism (for example, tetrahydrolipstatin, ezetimibe, cholestyramine, fibrates, or statins) either within the month preceding the study or during the study period. The study was approved by the regional committee on human experimentation (N°2008-A01354-51, Comité de Protection des Personnes Sud Méditerranée I, Marseille, France). Procedures followed were in accordance with the Declaration of Helsinki of 1975 as revised in 1983. Informed written consent was obtained from each participant.

Study design

This study constitutes a secondary analysis of data from a
randomized cross-over clinical trial dedicated to the identifica-
tion of genetic variants associated with the bioavailability of lipid
micronutrients, such as α-tocopherol, lycopene, β-carotene,
lutein, and phytofluene [34–38], where participants consumed 5
test meals containing or not the studied micronutrients. Adipose
tissue samples were taken during 3 of these 5 test meals, and not
during each test meal, because adipose tissue samples are rela-
tively invasive, and we did not want any participants leave the
study because of these samples. The 3 test meals during which the
adipose tissue samples were taken were chosen at random from
the 5 test meals. The test meals were consumed in a random order
and were separated by a washout period of ≥3 wk. The test meals
had the same basic composition, that is, 70 g of semolina cooked
in 200 mL of water, 40 g of white bread, 60 g of hard-boiled egg
whites, 50 g of peanut oil, and 330 mL of mineral water. They
only differed in their micronutrient content: the “control meal”
did not have any added source of micronutrients, the “vitamin E
meal” included a capsule containing RRR-α-tocopheryl acetate
equivalent to 67 mg (100 IU) α-tocopherol (Holland & Barrett)
[34], and the “tomato meal” included 100 g tomato puree, which
contained 0.41 mg provitamin A as β-carotene [35,36,38]. Par-
ticipants were asked to limit the consumption of VA-rich foods 48
h before each test meal (an avoidance list, for example, liver,
carrots, was provided to the participants). A day before each test
meal, participants were asked to eat dinner between 7 and 8 p.m.,
without any alcohol intake. They were also asked to abstain from
consuming any food or beverage other than water after the dinner
and until they came to the Center for Clinical Investigation (la
Conception Hospital). At arrival, a first adipose tissue biopsy was
collected by a trained physician. Participants then consumed 1 of the 3 test meals, and blood samples were taken at regular time intervals ≤8 h after consumption to measure the bioavailability of the test meal micronutrients (data not shown but previously published [34–38]). Participants were instructed to consume the meal at a broadly similar rate, with half of the meal to be consumed within the first 10 min, and the remainder within the next 10 min. No other food was consumed for the next 8 h, but participants were allowed to drink the rest of the water provided with the meal. After 8 h, a second adipose tissue biopsy was taken from the periumbilical region, following the same procedure as that described above.

## Adipose tissue biopsy

After local anesthesia with 1 cm³ nonadrenalized xylocaine (2%), ~100–200 mg subcutaneous adipose tissue was aspirated from the periumbilical region. The aspiration was performed with a manual and nontraumatic technique. Adipose tissue was harvested using a 10 mL Luer Lock syringe, through a 15 Gauge cannula (15 cm in length and 1.5 mm internal diameter). The cannula was removed and the collection site was compressed for 2 min to prevent bruising and to speed up the healing process. The sample was immediately placed into an ice-water bath and covered with aluminum foil to avoid light exposure. As explained previously, the adipose tissue sample being relatively invasive, we only carried it out during 3 test meals. Moreover, as there was no data in the literature on the potential variation in the concentration of lipid micronutrients in adipose tissue following the consumption of these micronutrients, we also sought to compare the concentrations before and after the consumption of these micronutrients. This is why a sample was taken at fast and 8 h after consumption of the 3 test meals.

## Biochemical measurements

Fasting plasma total cholesterol, triglyceride, glucose, and hemoglobin concentrations were measured at baseline using standard methods as previously described [37]: all analyte concentrations were measured on a Modular PP instrument (Roche Diagnostics), except for hemoglobin, which was measured on an ADVIA 120 instrument (Siemens Healthcare Diagnostics), at La Conception Hospital (Biochemistry Laboratory). All analyses were performed according to the manufacturers' instructions.

## Measurements of RET concentrations in plasma and adipose tissue

Plasma RET concentrations were measured as previously described [39]. Concerning adipose tissue RET, RET and retinyl esters [39,40] were first extracted as follows: around 50 mg (wet weight) of subcutaneous periumbilical adipose tissue was crushed in 300 µL of phosphate buffered saline with two 3-mm diameter stainless steel balls using an MM301 ball mill (Retsch). Then a 50 µL aliquot was collected for protein quantification by the BiCinchoninic acid Assay kit (Pierce) after dilution to 1/5 in phosphate-buffered saline buffer. Lipids, including VA, were extracted from the remaining 250 µL using 2 mL trichloromethane/methanol (1/1, v/v) and 0.9 mL phosphate buffered saline. All extractions were performed at room temperature under yellow light to minimize light-induced damage. The dried extract was incubated at 37°C for 1 h 30 min with 100 µL of an ethanolic pyrogallol solution (12%, w/v) and 1 mL of an ethanolic potassium hydroxide solution (5.5%, w/v) to saponify retinyl esters and quantify total RET. After incubation, the sample was cooled to room temperature and 100 µL of an ethanolic echinenone solution was added as an internal standard. The mixture was extracted twice by using 3 mL of hexane. The extract was completely evaporated under nitrogen and then solubilized in 100 µL methanol/dichloromethane (65/35, v/v) for downstream HPLC analysis.

A volume of 90 µL was injected for HPLC analysis. Separation was achieved using a 10 × 4.0 mm, 5 µm particle size C18 Zorbax guard column (Interchim) followed by a 250 × 4.6 mm, 5 µm particle size YMC C30 column (Interchim). The isocratic mobile phase was composed of 100% HPLC-grade methanol (Carlo Erba – SDS) maintained at 35°C with a flow rate of 1.5 mL/min. The HPLC system consisted of a 680 HPLC Pump, an ASI-100 Automated Sample Injector (Dionex SA) and a UVD340U photodiode array detector (Dionex SA). RET was detected via UV spectra and retention time coincident with authentic standard (Sigma-Aldrich) and quantitated at 325 nm. Peak integration and quantification were performed using Chromeleon CDS software (version 6.80, Dionex), using external calibration curves normalized to internal standard.

## DNA isolation and genotyping

An average of 25 µg of DNA was isolated from saliva samples using the Oragene kit (DNA Genotek). DNA preparation and genotyping methods were performed as previously described [37]. The whole genome was genotyped using Human-OmniExpress BeadChips (Illumina), which allowed for the analysis of ≈7.33 × 10⁵ SNPs distributed throughout the genome.

## Candidate SNP selection

We first carried out a literature search for genes whose encoded proteins could modulate VA concentration in adipose tissue, either directly, by being involved in adipocyte VA metabolism, or indirectly, by influencing blood VA concentration. This led us to select 27 candidate genes (Supplemental Table 1). Of the corresponding 1305 SNPs on the DNA chips, we first excluded SNPs for which the genotype call rate was <95% and SNPs that presented a significant departure from the Hardy-Weinberg equilibrium (*P* < 0.05; chi-squared test) (281 SNPs excluded), leaving 1024 SNPs. Then, for each candidate gene, we excluded SNPs in high linkage disequilibrium (LD, R² > 0.80), leaving 786 SNPs. LD information was retrieved using the LD TAG SNP Selection tool from the SNPinfo Web Server (accessible at https://snpinfo.niehs.nih.gov), considering genotype data from HapMap in the European (CEU) population. Finally, SNPs were tested under both additive and dominant models. The additive model assumes that there is a uniform, linear increase, or decrease in the quantitative trait for each copy of the minor allele. Hence, homozygotes for the major allele, heterozygotes and homozygotes for the minor allele were treated independently. For tests using the dominant model, participants homozygous for the lesser frequent allele were grouped with heterozygous participants and were compared with participants homozygous for the more frequent allele. SNPs with fewer than 5 observations in each group of every model were excluded from further analysis, leaving 191 and 619 SNPs in the additive and dominant models, respectively (Supplemental Figure 1).
# SNP function prediction

PolyPhen-2 within the Ensembl variant effect predictor (VEP) (accessible at https://www.ensembl.org/Tools/VEP) was used to predict the effect of the exonic variants as described previously [38]. The PolyPhen-2 score ranges from 0 to 1, with a value of 1 indicating the variant is more likely to be deleterious. Functional effects of other forms of genetic variants (intronic and intergenic variants) were predicted using RegulomeDB (accessible at: https://regulomedb.org). The RegulomeDB probability score ranges from 0 to 1, with a value of 1 indicating the variant is more likely to be a regulatory one [41].

# Statistics

## Power calculation

Note that it was not feasible to conduct a statistical power calculation to determine the number of participants required to detect a significant effect of a genetic polymorphism on RET concentrations in adipose tissue, as this analysis was based on secondary data. Therefore, this study should be considered as an exploratory one and the data generated will allow other researchers to carry out power calculation.

## Linear mixed model

Adipose tissue RET concentration data were analyzed using a linear mixed model with a full factorial design that included meal (control, vitamin E and tomato puree) and time (fasting and 8 h) as fixed within-subject factors and participant as a random factor. The variables studied were subjected to 5 covariance structures: unstructured covariance, scaled identity, diagonal, compound symmetry, and autoregressive order 1. The goodness of fit of the models was compared using Akaike's Information Criterion. Departure from normality was assessed using QQ plots, whereas departure from homoscedasticity was assessed both with Levene's test and by inspecting scatterplots of residuals.

Adipose tissue RET concentrations measured at fast and 8 h after consumption of the 3 test meals were tested for outliers by 2-tailed Grubbs' tests using an online calculator (accessible in https://www.graphpad.com/quickcals/grubbs1). Pearson's correlation coefficients were calculated with 95% confidence intervals (CI) to evaluate whether the mean adipose tissue RET concentrations were correlated with plasma RET concentrations and other basic blood parameters of participants. CV of plasma and adipose tissue RET concentrations were compared according to Forkman [42]. Values of *P* < 0.05 were considered significant. Statistical analyses were performed using SPSS 28 (SPSS Inc.).

## Multivariate analysis

PLS regression [43] was used to identify the combination of SNPs explaining best the variance in adipose tissue RET concentration. To compare adipose tissue RET concentrations between subgroups of participants who bore different genotypes at the candidate SNPs, the univariate filtering step consisted of selecting SNPs that showed a *P* value < 0.05 (Wald test asymptotic *P* value) using PLINK (v1.07, http://pngu.mgh.harvard.edu/purcell/plink/). PLS regression model coded in units of variance was then built. SNP variables that passed the univariate filtering were included in the PLS regression model. Variables were ranked according to their variable importance in the projection (VIP) value, which estimates the contribution of each

variable in the projection used in the PLS regression model, and several PLS regression models were then built using increasing VIP threshold values as described in detail elsewhere [44]. To find a combination of SNPs more predictive of this phenotype, SNPs and covariates that made a less important contribution, that is, those that displayed the lowest VIP values, were filtered out. An initial 7-fold cross-validation procedure was performed, in which the dataset for each generated multivariate PLS model was divided into 7 distinct subsets. Note that a cross-validation *k* value between 5 and 10 is usually regarded as a good compromise for the bias-variance trade-off because these values have been shown to present neither excessive variance nor excessive bias [45]. This is the reason why we used *k* = 7 which is the default option in the SIMCA software. The model maximizing the adjusted *R*², and having significant *P* value after cross-validation analysis of variance (ANOVA) [46], was selected. Robustness and stability of the selected model were verified by leave-*k*-out cross-validation [47], regression coefficient stability testing [37], *R*², and adjusted *R*² were tested after 100 random permutations of the *Y* variable, that is, adipose tissue RET concentration [48]. (see additional validations of the PLS regression model in Supplements). SIMCA Multivariate Data Analytics Solution software (Version 17.0.0.24543, Umetrics) was used for all multivariate data analyses and modeling.

# Results

## Adipose tissue RET concentration of the participants

Participants presented normal energy consumption, that is, ≈ 2500 kcal/d, and drank ≤2% alcohol as total energy. The baseline characteristics of the participants are shown in Table 1. As neither the sampling time (*P* = 0.321), that is, fasting or 8 h after the test meal, nor the test meal ingested (*P* = 0.174), that is, control, vitamin E or tomato puree, had a significant effect on adipose tissue RET concentration, we decided to treat all 6 values as technical replicates, thereby offering us the possibility to obtain a more accurate estimate of an individual's adipose tissue RET concentration. To this end, we calculated the mean of the adipose tissue RET concentration for the 3 test meals and the 2 time points for each of the 42 participants (maximum of 6 values) (Figure 1). Adipose tissue RET concentration exhibited a high interindividual variability, with a CV of 62%. This was significantly higher (*P* < 0.00001) than the interindividual variability of fasting plasma RET concentration, which had a CV of 16%.

All participants had fasting plasma RET concentrations higher than the cutoff value that suggests VA deficiency, that is, <0.7

<caption>TABLE 1</caption>

Baseline characteristics of the study participants (n = 42).

<table><thead><tr><td>Characteristic</td><td>Mean (SEM)</td></tr></thead><tbody><tr><td>Age (y)</td><td>31.3 (1.9)</td></tr><tr><td>Weight (kg)</td><td>73.6 (1.3)</td></tr><tr><td>BMI (kg/m²)</td><td>23.0 (0.3)</td></tr><tr><td>Total cholesterol (g/L)<fnref n="1" /></td><td>1.6 (0.1)</td></tr><tr><td>Triglycerides (g/L)<fnref n="1" /></td><td>0.8 (0.1)</td></tr><tr><td>Glucose (mmol/L)<fnref n="1" /></td><td>4.7 (0.1)</td></tr><tr><td>Hemoglobin (g/dL)<fnref n="1" /></td><td>15.0 (0.1)</td></tr><tr><td>Retinol (µmol/L)<fnref n="1" /></td><td>2.5 (0.4)</td></tr></tbody></table>

<footnote n="1">¹ Analytes were quantified in fasting plasma samples.</footnote>
<figure><img src="image_4.png" /><figcaption>FIGURE 1. Adipose tissue retinol (RET) concentration of the participants. Adipose tissue RET concentration (nmol/g proteins) of each participant was the mean of RET concentrations measured on samples taken at fast and 8 h after 3 test meals that did not contain significant amounts of vitamin A and that were ingested ≥3 wk apart. Values are means (n = 2–6) with their SEM. Participants (n = 42) were sorted by increasing adipose tissue RET concentration. Each bar on the x-axis represents a participant’s average value.</figcaption></figure>

μM. There was no significant correlation between adipose tissue RET concentrations and age [Pearson’s r = 0.006 (95% CI: -0.298, 0.310), P = 0.97], BMI [r = 0.101 (95% CI: -0.210, 0.393), P = 0.53], fasting plasma RET concentrations [r = -0.184 (95% CI: -0.488, 0.158), P = 0.28], fasting plasma triglyceride concentrations [r = 0.268 (95% CI: -0.039, 0.529), P = 0.09], fasting plasma cholesterol concentrations [r = 0.172 (95% CI: -0.140, 0.452), P = 0.28], or fasting plasma β-carotene concentrations [r = 0.005 (95% CI: -0.303, 0.312), P = 0.98].

## SNPs associated with the interindividual variability of adipose tissue RET concentration

We first measured the association between SNPs analyzed individually and adipose tissue RET concentration (Table 2). Seven SNPs (within 6 genes) exhibited a significant P value under the additive model, and 22 SNPs (within 9 genes) exhibited a significant P value under the dominant model. Unstandardized regression coefficients (B coefficients), which represent the mean change in adipose tissue RET concentration for each additional copy of the minor allele under the additive model, and in the presence of the minor allele under the dominant model, are provided in Table 2. Note that 1 missense SNP in polycystic kidney disease 1-like 2 (PKD1L2), namely rs6420424, was predicted as having a “benign” effect by PolyPhen-2. Furthermore, most of the intronic SNPs had high RegulomeDB scores, signaling a high probability that they are located within gene regulatory regions and may hence influence gene expression.

## Combinations of SNPs associated with the interindividual variability of adipose tissue RET concentration

As previously done [34–38,49], we used PLS regression to find the best combination of SNPs and covariates to explain the interindividual variability of adipose tissue RET concentration. As

shown in Supplemental Table 2, the model including all selected variables (29 SNPs) could explain a high part of the variance in this phenotype (R² = 0.76) but this estimation was positively biased, as illustrated by the low adjusted R² value (0.35), because of the high number of predictors included in the model. With the application of several VIP value thresholds, the best model obtained had an adjusted R² of 0.54. It included 16 SNPs (not in LD) in or near 9 genes (Table 3). The model was first validated by cross-validation ANOVA (P = 1.6 × 10⁻⁹) (Supplemental Table 2). Then, its robustness and stability were validated by 3 additional methods (Supplemental Table 3, Supplemental Figures 2 and 3).

## Genetic score to explain adipose tissue RET concentration

With the knowledge of a participant’s genotype at the 16 SNPs in the selected model, it was possible to calculate a participant’s adipose tissue RET concentration using the following equation (adjusted R² = 0.54):

$$ \text{Adipose tissue VA concentration} = 700.2 + \sum_{i=1}^{16} (r_i) \times \text{number of minor allele SNP}_i \quad (1) $$

with rᵢ the unstandardized regression coefficient of the ith SNP in the PLS regression model (provided in Table 3). When SNPs were entered under the dominant model, participants homozygous for the lesser frequent allele were grouped with heterozygous participants and the number of minor alleles for both these groups was considered to be 1.

## Discussion

The first observation of this study concerns the interindividual variability of RET concentration in adipose tissue. The
<table><caption>**TABLE 2**
SNPs significantly associated with adipose tissue RET concentration following univariate analysis.</caption><thead><tr><th>SNP</th><th>Gene</th><th>Alleles</th><th>Alternate allele frequency (European population)<sup>1</sup></th><th>Gene region<sup>2</sup></th><th>Unstandardized regression coefficient<sup>3</sup></th><th>P value<sup>4</sup></th><th>Variant effect prediction score<sup>5</sup></th></tr></thead><tbody><tr><td colspan="8"><strong>Additive model</strong></td></tr><tr><td>rs2921193</td><td>PPARG</td><td>G&gt;A</td><td>A = 0.47</td><td>Intron</td><td>323.8 ± 97.5</td><td>0.002</td><td>0.18</td></tr><tr><td>rs10882273</td><td>FFAR4</td><td>T&gt;C</td><td>C = 0.39</td><td>3' UTR</td><td>-273.6 ± 106.8</td><td>0.014</td><td>0.71</td></tr><tr><td>rs11791747</td><td>RXRA</td><td>A&gt;G</td><td>G = 0.34</td><td>Intergenic</td><td>274.8 ± 110.8</td><td>0.017</td><td>0.61</td></tr><tr><td>rs4888121</td><td>PKD1L2</td><td>T&gt;C</td><td>C = 0.43</td><td>Intron</td><td>-228.9 ± 101.5</td><td>0.023</td><td>0.70</td></tr><tr><td>rs6959775</td><td>CD36</td><td>A&gt;G</td><td>G = 0.56</td><td>Intergenic</td><td>-265.8 ± 120.4</td><td>0.033</td><td>0.13</td></tr><tr><td>rs3132330</td><td>RXRA</td><td>G&gt;A</td><td>A = 0.45</td><td>Intergenic</td><td>-224.4 ± 106.3</td><td>0.041</td><td>0.13</td></tr><tr><td>rs8099996</td><td>LDLR</td><td>A&gt;G</td><td>G = 0.36</td><td>Intergenic</td><td>234.0 ± 112.8</td><td>0.045</td><td>0.13</td></tr><tr><td colspan="8"><strong>Dominant model<sup>6</sup></strong></td></tr><tr><td>rs12629751</td><td>PPARG</td><td>C&gt;T</td><td>T = 0.09</td><td>Intron</td><td>469.7 ± 104.6</td><td>10<sup>-5</sup></td><td>0.18</td></tr><tr><td>rs1151996</td><td>PPARG</td><td>C&gt;A</td><td>A = 0.62</td><td>Intron</td><td>-262.4 ± 76.1</td><td>0.001</td><td>0.45</td></tr><tr><td>rs6778770</td><td>MGLL</td><td>A&gt;G</td><td>G = 0.24</td><td>Intron</td><td>239.7 ± 74.0</td><td>0.002</td><td>0.51</td></tr><tr><td>rs3773161</td><td>MGLL</td><td>C&gt;T</td><td>T = 0.97</td><td>Intron</td><td>362.8 ± 114.7</td><td>0.003</td><td>0.69</td></tr><tr><td>rs11632233</td><td>STRA6</td><td>C&gt;A</td><td>A = 0.62</td><td>Intergenic</td><td>220.6 ± 78.1</td><td>0.007</td><td>0.13</td></tr><tr><td>rs17108968</td><td>FFAR4</td><td>G&gt;A</td><td>A = 0.09</td><td>Intron</td><td>-264.4 ± 97.2</td><td>0.010</td><td>0.13</td></tr><tr><td>rs392267</td><td>STRA6</td><td>C&gt;A</td><td>A = 0.31</td><td>Intergenic</td><td>-204.2 ± 76.5</td><td>0.011</td><td>0.61</td></tr><tr><td>rs13076933</td><td>PPARG</td><td>T&gt;G</td><td>G = 0.26</td><td>2 kb upstream</td><td>202.6 ± 76.8</td><td>0.012</td><td>0.24</td></tr><tr><td>rs670345</td><td>DGAT2</td><td>G&gt;A</td><td>A = 0.07</td><td>Intergenic</td><td>-244.4 ± 98.5</td><td>0.017</td><td>0.61</td></tr><tr><td>rs10869208</td><td>ALDH1A1</td><td>C&gt;T</td><td>T = 0.31</td><td>Intergenic</td><td>-190.8 ± 77.8</td><td>0.019</td><td>0.59</td></tr><tr><td>rs17260769</td><td>CD36</td><td>T&gt;G</td><td>G = 0.47</td><td>Intergenic</td><td>215.6 ± 88.1</td><td>0.019</td><td>0.55</td></tr><tr><td>rs12925874</td><td>PKD1L2</td><td>G&gt;T</td><td>T = 0.28</td><td>Intron</td><td>-195.4 ± 81.0</td><td>0.020</td><td>0.18</td></tr><tr><td>rs8043708</td><td>PKD1L2</td><td>T&gt;C</td><td>C = 0.12</td><td>Intron</td><td>-192.0 ± 80.0</td><td>0.021</td><td>0.13</td></tr><tr><td>rs6420424</td><td>PKD1L2</td><td>G&gt;A</td><td>A = 0.47</td><td>Missense</td><td>196.5 ± 82.5</td><td>0.022</td><td>0.39</td></tr><tr><td>rs11716997</td><td>MGLL</td><td>G&gt;T</td><td>T = 0.57</td><td>Intergenic</td><td>-214.8 ± 91.4</td><td>0.024</td><td>0.36</td></tr><tr><td>rs11187533</td><td>FFAR4</td><td>C&gt;T</td><td>T = 0.27</td><td>Intron</td><td>-182.5 ± 78.3</td><td>0.025</td><td>0.61</td></tr><tr><td>rs940390</td><td>FFAR4</td><td>A&gt;G</td><td>G = 0.15</td><td>Intergenic</td><td>-201.5 ± 86.2</td><td>0.025</td><td>0.78</td></tr><tr><td>rs587779</td><td>MGLL</td><td>C&gt;T</td><td>T = 0.86</td><td>Intron</td><td>199.6 ± 89.0</td><td>0.031</td><td>0.78</td></tr><tr><td>rs12706949</td><td>CD36</td><td>G&gt;T</td><td>T = 0.43</td><td>3' UTR</td><td>-176.9 ± 80.8</td><td>0.034</td><td>0.67</td></tr><tr><td>rs11940892</td><td>LRAT</td><td>T&gt;C</td><td>C = 0.26</td><td>Intergenic</td><td>172.6 ± 82.3</td><td>0.042</td><td>0.18</td></tr><tr><td>rs7032984</td><td>RXRA</td><td>G&gt;A</td><td>A = 0.18</td><td>Intergenic</td><td>168.1 ± 81.3</td><td>0.045</td><td>0.67</td></tr><tr><td>rs6564842</td><td>PKD1L2</td><td>T&gt;G</td><td>G = 0.33</td><td>Intron</td><td>-161.7 ± 79.1</td><td>0.048</td><td>0.61</td></tr></tbody></table>

Abbreviations: A, adenine; C, cytosine; G, guanine; RET, retinol; SNP, single nucleotide polymorphism; UTR, untranslated region; T, thymine Gene names can be found in Supplemental Table 1.

¹ Alternate allele frequencies were retrieved from dbSNP (https://www.ncbi.nlm.nih.gov/snp/) using the Allele Frequency Aggregator (ALFA) dataset (pooled allele frequency data from dbSNP and the dbGaP) in the European population (5 September, 2022).

² Coding status: intron, missense, upstream, or UTR. The 3' UTR is a regulatory DNA region situated at the 3' end of all protein-coding genes that is transcribed into mRNA but not translated into protein. All SNPs were otherwise intergenic.

³ Unstandardized regression coefficients represent the mean change in adipose tissue RET concentration (nmol/g protein) for each additional copy of the minor allele under the additive model and in the presence of the minor allele under the dominant model.

⁴ SNPs are ranked by increasing *P* values.

⁵ Variant Effect Prediction Score was estimated using https://www.ensembl.org/Homo_sapiens/Tools/VEP/ for exonic/missense and RegulomeDB for intron, upstream, UTR or intergenic SNPs (accessed on 5 September, 2022).

⁶ For SNPs under the dominant model, participants homozygous for the lesser frequent allele were grouped with heterozygous participants and were compared with participants homozygous for the more frequent allele.

coefficient of variation, 62%, was obtained for a relatively homogeneous group of participants, that is, adult males, healthy weight range, VA-sufficient, suggesting that it may be even higher in the general population. Our result is comparable to that of measures of variability reported in more recent literatures as shown in Table 4 [50,51] We also suggest that it could be even higher if we compared populations of different ethnicities, that is, with different genotypic frequencies, and/or populations comprising subjects more or less VA deficient, as is the case in many developing countries. The second observation was that adipose tissue RET concentration was not associated with blood RET concentration (Supplemental Table 4). This is not surprising because blood RET concentration is tightly regulated (homeostatic control, with CV of 16%) whereas such regulation is not

supposed to occur in adipose tissue [4,29,52]. The third observation was related to the lack of correlation between adipose tissue RET concentration and other measured participant variables, that is, age, BMI, and blood parameters. Although a reduction of adipose tissue VA concentration has previously been reported during obesity in animals [53], all participants of our study were in a healthy range of BMI.

The main result of our study consisted in the identification of several genes putatively involved in the regulation of VA concentration in adipose tissue, based on the association between SNP in these genes and adipose tissue RET concentration. The association with *STRA6* was the most anticipable. Indeed, this gene encodes a transmembrane protein involved in the anchoring of the blood RET/RBP4/TTR complex to several
<table><caption>**TABLE 3**
Combination of 16 SNPs associated with adipose tissue RET concentration in the participants.</caption><thead><tr><th>Gene<sup>1</sup></th><th>SNP</th><th>VIP value<sup>2</sup></th><th>Regression coefficient<sup>3</sup></th></tr></thead><tbody><tr><td>PPARG</td><td>rs12629751</td><td>1.53</td><td>247.0</td></tr><tr><td>PPARG</td><td>rs1151996</td><td>1.27</td><td>138.0</td></tr><tr><td>PPARG</td><td>rs2921193</td><td>1.23</td><td>85.1</td></tr><tr><td>MGLL</td><td>rs6778770</td><td>1.21</td><td>126.0</td></tr><tr><td>MGLL</td><td>rs3773161</td><td>1.18</td><td>190.8</td></tr><tr><td>STRA6</td><td>rs11632233</td><td>1.08</td><td>-116.0</td></tr><tr><td>FFAR4<sup>4</sup></td><td>rs17108968</td><td>1.05</td><td>-139.0</td></tr><tr><td>STRA6</td><td>rs392267</td><td>1.03</td><td>107.4</td></tr><tr><td>PPARG</td><td>rs13076933</td><td>1.02</td><td>106.5</td></tr><tr><td>FFAR4</td><td>rs10882273</td><td>0.99</td><td>-71.9</td></tr><tr><td>DGAT2</td><td>rs670345</td><td>0.97</td><td>-128.5</td></tr><tr><td>RXRA</td><td>rs11791747</td><td>0.97</td><td>72.3</td></tr><tr><td>ALDH1A1</td><td>rs10869208</td><td>0.96</td><td>100.3</td></tr><tr><td>CD36</td><td>rs17260769</td><td>0.95</td><td>-113.4</td></tr><tr><td>PKD1L2</td><td>rs12925874</td><td>0.94</td><td>-102.8</td></tr><tr><td>PKD1L2</td><td>rs8043708</td><td>0.94</td><td>-101.0</td></tr></tbody></table>

Abbreviations: RET, retinol; SNP, single nucleotide polymorphism

¹ Gene names can be found in Supplemental Table 1.

² Variables were ranked according to their variable importance in the projection (VIP) value, which estimates the contribution of each SNP in the projection used in the PLS regression model.

³ Regression coefficients are for untransformed variables and represent the mean change in adipose tissue RET concentration (nmol/g protein) for each additional copy of the minor allele under the additive model and in the presence of the minor allele under the dominant model.

tissues [31,54], and participates in the uptake of RET by tissues [55]. In adults, *STRA6* has been previously shown to be significantly expressed in adipose tissue, especially when excessive amounts of VA are present in blood [56], suggesting its involvement in RET homeostasis by storing excess VA in adipose tissue.

Another association that was anticipated is the 1 with cluster of differentiation 36 membrane receptor. Indeed, this scavenger receptor is able to bind several ligands, including long-chain fatty acids, oxidized lipids and fat-soluble vitamins [57,58], proVA carotenoids [33,59], as well as lipoproteins. Therefore, we hypothesize its indirect involvement in VA uptake by adipose tissue via its role in the uptake of lipoproteins containing proVA carotenoids, that is, chylomicron remnants and LDL.

The association with *ALDH1A1* was remarkable. Indeed, the enzyme encoded by this gene, that is, the aldehyde dehydrogenase 1 family member A1, catalyzes the final oxidative step needed for the synthesis of retinoic acid from RET [60]. Therefore, we hypothesize that a lower expression or activity of this enzyme, could increase the concentration of RET in adipose tissue.

With regard to VA storage in adipocytes and the mobilization of the stored vitamin when needed, we have also found associations between RET concentration in adipose tissue and SNPs in *DGAT2* and *MGLL*. Indeed, *DGAT2* encodes for acyl CoA:d-acylglycerol acyltransferases 2, which is involved in the synthesis of triglyceride but also in the esterification of RET [61]. *MGLL* encodes for MGLL [62], which belongs to a group of lipases responsible for the mobilization of adipose tissue VA stores, that is, retinyl esters (reviewed in [63]). It is important to keep in mind that these 2 enzymes are involved in the metabolism of triglycerides in adipocytes and that retinyl esters, which are fat-soluble molecules, are possibly stored in triglyceride-rich lipid droplets. Thus, the effect of SNP in these genes could impact the amount of triglycerides available to store retinyl esters. This last hypothesis could also explain the associations with the transcription factors, retinoid X receptor alpha and peroxisome proliferator activated receptor gamma. Indeed, these transcription factors regulate many genes and metabolic pathways, including fatty acid oxidation. It can therefore be assumed that genetic variations that affect the activity or expression of these transcription factors could also modulate the amount of triglycerides available to store retinyl esters. Nevertheless, this is only 1 hypothesis among others given the very large number of genes that are modulated by these transcription factors.

The association of SNPs in free fatty acid receptor 4 (*FFAR4*) and *PKD1L2* with adipose tissue RET concentration is not easy to understand, even though these genes have been associated with VA metabolism in previous studies [36,64]. *FFAR4* (also known as *GPR120*) is a fatty acid receptor with a specificity for medium-to long-chain fatty acids and it is involved in metabolic homeostasis, including adipose metabolism and inflammatory regulation [65]. The association found may thus result from the crosstalk of fatty acids and triglycerides with VA metabolism, notably its esterification and storage in adipose tissue. It is also interesting to note that *FFAR4* is located directly downstream of *RBP4*, which encodes for the blood transporter that delivers RET

<table><caption>**TABLE 4**
Interindividual variability of adipose tissue RET concentration in selected observational studies.</caption><thead><tr><th>Author/s</th><th>Publication year</th><th>n, Population</th><th>Mean</th><th>SD</th><th>% CV<sup>1</sup></th><th>Ref.</th></tr></thead><tbody><tr><td>Kardinaal et al.</td><td>1995</td><td>85 healthy participants from the Netherlands (47 females and 38 males)</td><td>2.35 µg/g fatty acid<sup>2</sup><br>2.99 µg/g fatty acid<sup>3</sup></td><td>1.30<br>3.08</td><td>55.3<br>103.0</td><td>[50]</td></tr><tr><td>Virtanen et al.</td><td>1996</td><td>409 nonsmokers and healthy participants from Finland, Germany, Israel, the Netherlands, Norway, Russia, Spain, Switzerland, and the United Kingdom (208 females and 201 males)</td><td>1.62 µg/g fatty acid<sup>2</sup><br>1.78 µg/g fatty acid<sup>3</sup></td><td>1.03<br>1.06</td><td>63.6<br>59.6</td><td>[26]</td></tr><tr><td>Casal et al.</td><td>2001</td><td>140 healthy participants from Portugal</td><td>0.43 µg/g adipose tissue<sup>4</sup></td><td>0.26</td><td>59.8</td><td>[51]</td></tr></tbody></table>

Abbreviations: CV, coefficient of variation; RET, retinol

¹ The value of %CV was calculated, when SD or min-max values was reported.

² The indicated value pertains solely to female participants.

³ The indicated value pertains solely to male participants.

⁴ The value provided included participants of both genders.
from the liver to target tissues [14]. We therefore hypothesize
that the associations between the 2 SNPs assigned to *FFAR4* and
adipose tissue RET concentration could be because of genetic
variation(s) in a regulatory region of the *RBP4* gene, in linkage
disequilibrium with these 2 SNPs assigned to *FFAR4*. This hy-
pothesis is supported by the observation that the minor C-allele
of *FFAR4*-rs10882273 was significantly associated with reduced
plasma RBP4 levels in a large-scale population-based sample of
Chinese Hans [66]. Furthermore, *FFAR4*-rs10882273 exhibits
strong LD (R² = 0.85) with rs10882283 in the *RBP4* gene, which
has been associated with circulating RET concentrations in the
largest genome-wide association study of RET conducted in
individuals of European ancestry, involving ≤22,274 partici-
pants [67].

A similar hypothesis may be proposed to explain the associ-
ation between adipose tissue RET concentration and SNPs
assigned to *PKD1L2*. Indeed, it has been observed that some SNPs
overlap both *PKD1L2* and *BCO1* [68,69]. As *PKD1L2* is not
known to have an effect on VA metabolism, we hypothesized
that the SNPs assigned to *PKD1L2* affect the expression or ac-
tivity of *BCO1*, which is a key gene in VA metabolism [70]. This
hypothesis is further supported by the fact that 1 of these 2 SNPs,
rs8043708, has been previously associated with the postprandial
chylomicron β-carotene response [36]. If our hypothesis is cor-
rect, this suggests that the enzyme *BCO1* present in adipose

tissue transforms significant amounts of proVA carotenoids into
retinal, which is then metabolized into RET, as what has been
previously shown in murine adipocytes [71].

We are fully aware of the limitations of this study. Indeed,
adipose tissue RET concentration is a complex phenotype that
results from RET and proVA carotenoid uptake and their com-
plex metabolism in this tissue. Many genes are consequently
involved and thus, a thorough investigation of the association
between genetic variants and this phenotype should ideally
include all SNPs in these genes. Yet, several SNPs in candidate
genes were not entered in the PLS regression analysis because
either they were not expressed on the BeadChips or they were
excluded from the analysis (as explained in the Methods).
Additionally, we followed a candidate gene approach and
excluded genes not expressed in the adipose tissue, whose as-
sociation with this phenotype is not yet known, may have been
left out of the analysis. Also, because only Caucasian males
participated in this study, the associations observed need to be
validated externally in females as well as in other ethnic groups,
preferably with a larger sample size to be generalized. Never-
theless, this study allowed us, for the first time to our knowledge,
to identify genes and SNPs that are associated with RET con-
centration in human adipose tissue (schematized in Figure 2) on
the basis of previous genetic studies of VA metabolism. Although
we are aware of its limitations, we believe that it will serve as a

<figure><img src="image_5.png" /></figure>

FIGURE 2. Genes whose single nucleotide polymorphisms (SNPs) were associated with the interindividual variability of adipose tissue retinol (RET) concentration. The associations were assessed by univariate analysis. *P* values and variant effect prediction scores are shown in Table 2. The genes in white on a black background are those that have been associated with RET concentration in adipose tissue. \*None of the SNPs in *RBP4* that we retained in the statistical analyses were associated with RET concentration in adipose tissue. Nevertheless, we hypothesized in the discussion that the 2 SNPs of *FFAR4*, which is not known to have a role in VA metabolism, are in linkage disequilibrium with 1 or more SNPs in a regulatory region of *RBP4*. Indeed, *FFAR4* is located just upstream of *RBP4*. \*\*It has been shown that some SNPs overlap both the *PKD1L2* and *BCO1* genes. As *PKD1L2* is not known to have an effect on VA metabolism, we hypothesized that these SNPs, assigned to *PKD1L2*, affect the expression or activity of *BCO1*, which is a key gene in VA metabolism. The full names of the genes are shown in Supplemental Table 1.
basis for further studies on the identification of the main genes and genetic variants that modulate VA concentration in this tissue. We have proposed, for the first time to our knowledge, a genetic score that could be used to estimate the adipose tissue RET status of an individual, knowing his genotype at the identified SNPs. Obviously, it needs to be improved and validated in subsequent studies to become more accurate and be applicable to more populations. It could then be used to identify subjects at risk of having low adipose tissue RET concentrations and hence a greater risk of developing obesity and diabetes [10,30,72-74]. They could then be advised to consume more foods rich in VA and proVA carotenoids to compensate for their low capacity to accumulate VA in their adipose tissue.

## Acknowledgments

We thank the Department of Science and Technology-Science Education Institute, Philippines (DOST-SEI) and the Embassy of France to the Philippines and Micronesia for the PhilFrance-DOST Fellowship of MPZ. For the purpose of Open Access, a CC-BY public copyright license (Creative Commons — Attribution 4.0 International — CC BY 4.0) has been applied by the authors to the present document and will be applied to all subsequent versions up to the Author Accepted Manuscript arising from this submission.

## Author contributions

The authors' responsibilities were as follows - MPZ: data curation, formal analysis, investigation, methodology, software, validation, visualization, and cowriting—original draft; CD: conceptualization, data curation, formal analysis, investigation, methodology, supervision, validation, and cowriting—original draft; JFL: conceptualization, visualization, and writing—review and editing; BG: methodology and formal analysis; MN: investigation, methodology, and formal analysis; DOA: investigation and methodology; PB: conceptualization, funding acquisition, investigation, methodology, project administration, resources, supervision, visualization, writing—original draft, writing—review and editing; and all authors: read and approved the final manuscript.

## Conflict of interest

All authors report no conflicts of interest.

## Funding

This study was supported by the European Community's Sixth Framework Program. The funding was attributed to the Lycocard project (no. 016213), which was an Integrated Project within the framework of the "Food Quality and Safety" program.

## Data availability

Data described in the manuscript will not be made available because this was not stated in the ethics application.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.tjnut.2024.10.035.

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