Prediction algorithms using genetic and non genetic factors inducing vitamin D deficiency among healthy adults

Prediction algorithms using genetic and non genetic factors inducing vitamin D deficiency among healthy adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction algorithms using genetic and non genetic factors inducing vitamin D deficiency among healthy adults Mariem AMMAR, Amani ABDERRAHMANE, Syrine HENI, Mohamed Sahbi TIRA, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4448996/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Objective An alarming increase in vitamin D (vitD) deficiency even in sunny regions highlights the need for a better understanding of the mechanisms controlling vitD variability. We aimed to study potential variables involved in vitD deficiency among healthy Tunisian adults in order to establish two prediction algorithms: a composite algorithm (CA) that included genetic and non genetic factors and a simple one (SA) including only environmental non genetic factors. These algorithms could be used to predict vitD status and help identify individuals at high risk of vitD deficiency. Methods We screened six key genes (DBP, CYP2R1, CYP27B14, CYP24A1 and VDR) within the vitD metabolic pathway using 15 single nucleotide polymorphism (SNP) markers in across a cohort of 394 unrelated healthy individuals. After giving an informed consent, all participants were asked to complete a generalized questionnaire. Significant confounding factors that may influence the variability in serum 25(OH)D levels were used as covariates for association analyses. Statistical study was carried out with SPSS26.0. Results VitD deficiency correlated positively with albumin (r = 0.135, p = 0.007) and negatively with serum PTH (r = − 0.303, p < 0.001), age (r = − 0.198, P < 0.001), and BMI (r = − 0.143, p = 0.04). Multivariate logistic regression revealed that season, sun screen use, phototype, age, VDR- rs2228570 and CYP24A1- rs6013897 were significant predictors of hypovitaminosis D. Non genetic factors explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR- rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, genetic and non genetic factors contributed up to 27.6% in 25(OH)D concentrations variability. Conclusion 25(OH)D deficiency is highly prevalent among healthy adults in Tunisia. It is related to seasonal fluctuations, increasing age, darker skin tones, excessive sunscreen usage, and genetic polymorphisms in the VDR and CYP24A1 genes. The genetic markers could be used as tools in Mendelian randomization analyses of vitD, and they should well be considered when establishing a supplementation protocol in order to prevent 25(OH)D deficiency in the Tunisian population. vitamin D deficiency polymorphisms prediction algorithm Introduction/Background VitD is a fat soluble sunshine vitamin that plays an essential role in sustaining the skeletal integrity by maintaining phosphate and calcium homeostasis and regulating bone metabolism ( 1 , 2 ). The global prevalence of vitD deficiency appears to be increasing, and it has been linked to skeletal abnormalities, rickets and growth problems in children, and to osteomalacia, osteopenia, osteoporosis and skeletal fractures in adults. It is also associated with extra-skeletal diseases including autoimmunity, cancer, respiratory diseases, neurologic disorders and other adverse outcomes( 3 , 4 ). Hypovitaminosis D has become a pandemic with a myriad of health consequences and is being observed in all ethnicities and age groups worldwide( 3 , 5 ). Great controversy exists with regards to the optimal serum 25OHD concentration for bone health ( 3 , 5 , 6 ). Some guidelines, including the Institute of Medicine (IOM) guidelines reported little or no additional benefit with concentrations > 20 ng/ml( 7 ). However, a concentration of 30 ng/ml or greater was recommended by the Endocrine Society( 1 ), the Osteoporosis Research and Information Group( 8 ), the National Osteoporosis Society( 2 ) and the international osteoporosis foundation( 9 ). The dietary source of vitD is poor and approximately 90% of its supply originates from sunshine-induced skin synthesis( 1 , 3 ). After exposure to UVB rays (290–320 nm) 7-dehydrocholesterol (7-DHC) in the skin epidermis is isomerized to pre-vitamin D3 (cholecalciferol) which undergoes two hydroxylations by cytochrome P450 enzymes. The first one occurs in the liver by 25-hydroxylase enzyme, producing 25(OH)D, the main circulating form of vitD. The next hydroxylation occurs in the distal tubules of the kidney by 1-hydroxylase enzyme producing the biologically active 1, 25-dihydroxyvitamin D (1,25(OH) 2 D). Both 25(OH)D and 1,25(OH) 2 D can be inactivated after being hydroxylated at C24 by cytochrome P450 enzyme (CYP24A1). The majority of 25(OH)D and 1,25(OH) 2 D circulate in plasma bound to specific transport proteins called vitD binding protein (DBP) while a small fraction is free. The circulating concentration of vitD is tightly regulated and acts through a specific receptor (VDR) to mediate its genomics actions.( 4 , 10 ) The variability in vitamin D status and the severity of its deficiency are likely to be multifaceted, depending on various genetic, environmental and personal factors ( 11 , 12 ). Environmental factors, such as ethnicity, latitude, sun exposure and season, determine whether there is sufficient UVB radiation to stimulate dermal vitD synthesis( 13 , 14 ). Personal factors including gender, age, skin pigmentation, use of high SPF (sun protection factor), physical activity, clothing habits and dietary habits can influence individual vitD status( 15 , 16 ). Great interest has now turned to the gene-environment interactions that could have an impact on vitD bioavaibility and therefore a clear understanding of the genetic factors involved in determining vitD status is necessary to appreciate the possible gene-environment interactions of vitD. ( 17 – 19 ) In Tunisia, very few studies were designed to investigate the prevalence of hypovitaminosis D and most reported data were from studies in correlation with vitD deficiency related diseases in rather small number of patients. The objectives of this study were (i) to assess the prevalence of hypovitaminosis D, (ii) to define its associated risk factors among healthy adults, and (iii) to develop algorithms assessing individual vitD status. Methods Subjects and Study design The study was carried out in Sahloul University Hospital Center where we recruited a cohort of 394 health care personnel who were free from any condition affecting bone health, general nutrition and growth. Subjects with any chronic illness involving the liver and kidney or causing malabsorption, who used steroids, anticonvulsants, or vitD treatment, multivitamins or any medication that might affect calcium and vitD metabolism, were excluded. All individuals were asked to complete a generalized questionnaire that was based on the study by Berquist et al. ( 20 ), it included present and past medical history, medical use, pain during walking, recruitment season (Mars-April-May: Spring; June-July-August: Summer; September-October-November: Autumn and December-jannuary-february: Winter), skin pigmentation which was assessed using a 6 -point Fitzpatrick scale ( 21 ) (Light-skinned individuals were characterized as skin type I,II, III and dark-skinned individuals were classified as skin type IV, V, VI) age, gender, physical activity, clothing habits, housing conditions, sun exposure frequency, parts of the body that are typically exposed to sunlight (head, face, hands and other parts of the body) and frequency of sunscreen use. A validated food-frequency questionnaire was used in order to determine dietary habits and vitD intake (participants were asked to keep non-consecutive 3-day food intake record including one weekend day or holiday, portion sizes and preparation methods were assessed. After converting each food item into weight (g), the corresponding nutritional composition was obtained from standard food composition tables, and each participant's total dietary intake of macro- and micronutrients was assessed by Diététique 5.3(free online software). All participants submitted written informed consents before inclusion. All methods of sampling and protocols were approved by the Ethics Committee of Sahloul University Hospital and were performed in accordance with the relevant guidelines of the Helsinki Declaration. Demographic, anthropometric Parameters and Biochemical analysis Demographic variables included age and gender. Participants were categorized by age into young adults ( 50 years). Anthropometrics parameters included weight, height and Body mass index (BMI: kg/m 2 ). Overweight was defined as BMI ≥ 30 kg/m 2 . Biochemical factors including fasting glucose, serum calcium, phosphate, sodium, potassium, urea, creatinine, albumin, aspartate transaminase (ASAT), Alanine transaminase (ALAT) alkaline phosphatase (PAL), total cholesterol (TC), triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) were all measured with the use of an automatic biochemistry analyzer (Beckmann Coulter, Fullerton, CA, USA). Low density lipoprotein cholesterol (LDL-C) was estimated using Friedewald formula ( 22 ). Parathyroid hormone (PTH) and insulin fasting concentrations were measured with electrochemiluminescence immunoassay on COBAS e411 (Roche Diagnostics GmbH, Mannheim, Germany). HbA1c was measured by HPLC (VARIANT II BIO-RAD, Hercules, CA, USA). Assessment of circulating 25(OH)D concentration Serum 25(OH)D was quantified by an automated chemiluminescent immunoassay performed on COBAS e411 (Roche Diagnostics GmbH, Mannheim, Germany). According to the manufacturer’s specifcations, the intra- and inter-assay coefficients of variation were 5.4% and 8.4%, respectively with a functional sensitivity of 3 ng/mL. Genetic analysis DNA extraction was performed using salting-out DNA extraction protocol( 23 ). Genotype analysis was carried out by Polymerase Chain Reaction (PCR) using a PROFLEX PCR System (Applied Biosystems by Life Technologies) followed by Restriction Fragment Length Polymorphism (RFLP) assay. Fifteen candidate SNPs from six vitD pathway genes were selected from the International HapMap Project according to the following criteria: ( 1 ) biological importance in vitD metabolism, transportation, or degradation; ( 2 ) evidence of a significant association in previous GWASs and ( 3 ) on minor allele frequencies (MAF > 10%). The selected SNPs were: [DBP (rs4588, rs7041), CYP2R1 (rs2060793, rs10766197, rs12794714, rs10741657), CYP27B1 (rs10877012), CYP24A1 (rs6013897), DHCR7/NADSYN (rs3794060, rs12785878, rs3829251) and VDR (rs1544410, rs731236, rs7975232, rs2228570)]. Statistical analysis The statistical analysis was performed using the SPSS v23 software. The Hardy-Weinberg equilibrium was verified by the chi-square test. The quantitative results were compared using ANOVA test, Student test, or Mann-Whitney test if not Gaussian distribution. The qualitative variables, expressed in frequency (N) and in percentage (%), have been compared by the chi-square test and their association with vitD status were evaluated by Odds ratio (OR) with 95% confidence intervals (CI) and adjusted for potential confounding factors (p < 0.25) by binary logistic regression. The coefficient of correlation (r) was determined. To further evaluate the joint associations of demographic, lifestyle and genetic factors with the risk of vitD deficiency, binary multivariate logistic regression with stepwise backward selection method was used to arrive at the final model including only the most associated factors with vitD deficiency. To establish the predictive algorithms, we defined a randomly selected 80% derivation cohort and a validation test cohort made up of the remaining 20% of the total study population. A multiple linear regression was applied using the enter method to generate the algorithms, that would be easier to use in routine clinical practice (simple equation). We have considered 25 (OH)D concentration as a dependent variable and all variables showing significant association (p ≤ 0.05) as independent variables to be included in the composite algorithm (CA) that included genetic and non genetic factors. The simple algorithm (SA) was defined including only subject’s sun exposure degree and demographic variables. The performance of these algorithms was evaluated by calculating the coefficient of determination (intercept) and the standardized regression coefficient (Beta) to estimate the variability explained by each model. Predicted mean 25(OH)D concentration = Intercept + ∑ Beta (variable) × value (variable). We calculated afterwards a root mean square prediction error (MPE) to quantify the average absolute difference between observed and predicted serum 25(OH)D concentrations. The predicted concentration was estimated according to (SA and CA) and then compared to the initial 25(OH)D concentration. For all statistical tests mentioned above, a p < 0.05 was considered significant. Results Characteristics of participants and prevalence of VitD deficiency Among the studied subjects, 70.1% were female, 33.2% were aged above 50 years, 20% were considered as obese (BMI ≥ 30) and 65.2% of the participants had a dark-colored skin (type IV to VI). The present study showed a remarkably high prevalence of vitD deficiency among health care personnel. 92.1% were vitD deficient (25(OH)D 30ng/ml). We observed statistically significant differences in vitD deficiency rates with respect to gender, age and BMI. 25(OH)D concentrations were significantly lower among women than men (12.90[0-76.40] vs 22.20[5.3–52.5]ng/ml; p = < 0.001). The median 25(OH)D value was lowest among overweighed (11.60 [0–57] vs 15.50 [0-76.40]ng/ml; p = < 0.001), older subjects (13.10 [0-65.70] vs 16.20 [0-76.40]ng/ml; p = 0.003) and during cold season (13.70 [0-58.10]vs 16.30 [3.50–76.40] ng/ml; p = < 0.001). Table 1 summarizes the main characteristics of the study participants and the distribution of vitD status according to the studied parameters. Table 1 Distribution of vitamin D status according to epidemiological and anthropometric characteristics *p < 0,05. Characteristics Vitamin D (ng/ml) Mean ± standard deviation or Median [min-max] P Vitamin D Deficient 39.46 ± 11.87 < 0.001* Non Deficient 14.68 ± 7.05 Gender Male 22.20 [5.3–52.5] < 0.001* Female 12.90 [0-76.40] Age 50 years 13.10 [0-65.70] BMI < 30 kg/m² 15.50 [0-76.40] 30 kg/m² 11.60 [0–57] Season Hot season (Spring + Summer) 16.30 [3.50–76.40] < 0.001* Cold season (Automn + Winter) 13.70 [0-58.10] Factors associated with 25(OH)D concentration In simple regression analysis VitD concentration correlated positively with albumin (r = 0.135, p = 0.007) and negatively with serum PTH (r = − 0.303, p < 0.001), age (r = − 0.198, P < 0.001), and BMI (r = − 0.143, p = 0.04). Univariate analyses showed significant associations between vitD status and season, gender, age, BMI, sun screen use, phototype, veil, covering clothing, physical activity and albumin. All these factors fulfilled the criteria p-value < 0.25 and were considered as potential confounding factors for genotypic analyses. Genotype associations with VitD deficiency were adjusted to the fore-mentioned confounding factors so that we only obtain the specific effect of the studied SNPs. According to dominant model and after adjustment by binary logistic regression, we noted that there was a significant association of vitD deficiency according to genotype for two SNPs: CYP24A1-rs6013897 and VDR-rs2228570. The results revealed that OR of vitD deficiency associated with homozygote variant genotypes were 3.16 (IC95%: 1.7–5.87; p < 0.001)] and 2.89 (IC95%: 1.15–7.3; p = 0.024)] respectively. These associations were significant for the heterozygote genotypes as well. (Table 2 ) Table 2 VitD deficiency according to SNPs genotypes. SNP Non Deficient (%) Deficient (%) P crudeOR IC 95% P OR ƚ IC 95% P rs2228570 AA 13 (41,9%) 27 (7,4%) < 0.001* 1 - - 1 - - AG 10 (32,3%) 147 (40,5%) 7.07 [2.88–17.77] < 0.001* 2.84 [1.53–5.28] < 0.001* GG 8 (25,8%) 189 (52,1%) 11.37 [4.31–29.96] < 0.001* 3.16 [1.7–5.87] < 0.001* rs6013897 TT 17 (54,8%) 102 (28,1%) 0.008* 1 - - 1 - - AT 11 (35,5%) 215 (59,2%) 3.25 [1.47–7.2] 0.003* 2.89 [1.15–7.3] 0.024* AA 3 (9,7%) 46 (12,7%) 2.55 [1.01–9.15] 0.039* 1.68 [1.03–3.41] 0.049* ƚ OR of vitD deficiency associated to genotypes and adjusted for potential confounders (season, gender, age, BMI, sun screen use, phototype, veil, covering clothing, physical activity and albumin.); *p < 0.05. To assess the co-influence of non genetic and genetic risk factors on vitD status, we carried out multivariate analyses by backward conditional binary logistic regression where season, sun screen use, age, VDR-rs2228570 and CYP24A-rs6013897 were retained as the most significant factors associated with vitD deficiency. Prediction algorithms To establish the predictive algorithms, we defined (CA): a composite algorithm that included genetic and non genetic factors and (SA): a simple algorithm including only environmental non genetic factors. Multiple linear regression analysis resulted in the following algorithms of predictive 25(OH)D concentration: CA Predicted 25(OH)D concentration = 35.381 – (“number of rs6013897 variant allele(s)” × 5.826) – (“number of rs2228570 variant allele(s)” × 6.265) – (‘‘dark phototype’’ × 6.349) – (“sun screen use” × 3.584) – (“cold season” × 2.939) – (“age (years)” × 2.150). SA Predicted 25(OH)D concentration = 31,412 – (‘‘dark phototype’’ × 8,308) - (“sun screen use” × 3,341) - (“cold season” × 3,354) - (“age (years)” × 0,123). Model Performance In order to evaluate the performance of our predictive algorithms, we calculated a root mean square prediction error (MPE) to quantify the average absolute difference between observed and predicted serum 25(OH)D concentrations according to the two predictive models. There was no significant difference between the observed 25(OH)D concentration and those predicted by the two algorithms (CA p = 0.736 and SA p = 0.481). Using the predictive algorithms, we also obtained low percentages of difference compared to the observed concentration (CA MPE = -8.2%; SA MPE = 17.9%). Nevertheless, a marginal superiority in performance was evident for the CA. Genetic and non genetic factors contribution on vitD status Multiple linear regression revealed that season, sun screen use, phototype and age explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR-rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, genetic and non genetic factors explained up to 27.6% of the variance in 25(OH)D concentrations. (Table 3 ) Table 3 Genetic and non genetic factors contribution on vitD deficiency Variable p R² Cumulative R² Genetic factors rs2228570 < 0.001 9.5% 12% 27.6% rs6013897 0.002 2.5% Non genetic factors Phototype < 0.001 7.4% 15.6% Season < 0.001 4% Sun screen use < 0.001 3.5% Age 0.008 0.7% Discussion In our previous study ( 24 ), we focused on evaluating the impact of specific polymorphisms within pivotal genes of the vitamin D metabolic pathway on the efficacy of supplementation. Our focus shifts in this study to investigate vitD basal status among healthy adults in Tunisia, while also exploring the myriad factors that contribute to variations in its concentration. Our study showed that the prevalence of vitD deficiency state was up to 92.1% which was in line with results from other studies in healthy populations ( 5 , 25 ), including small studies of subgroups ( 26 ). Tunisia is located at latitude 35°49’34’’N, longitude 10°38’24’’E and on an average elevation of 246 meters above sea level, with relatively constant sunlight even in wintertime. In the present study, we observed higher serum 25(OH)D concentrations in males than in females (p < 0.001). This can likely be explained by differences in lifestyle between men and women, particularly the longer time spent outdoors by men( 4 ). Additionally, women are at least three times more likely than men to use sunscreen daily and therefore have less skin exposure to the sun ( 27 ). Our study showed that age was related to serum 25(OH)D concentration after adjustment for confounding factors. Similar results were observed in the study of Lui et al. where Serum 25(OH)D concentration were 3.7 nmol/l lower in old (≥ 60 years) compared with young adults(18–59 years) ( 28 ). Prior studies have also shown that increasing age is commonly associated with an increased risk of vitD deficiency ( 28 – 30 ), attributing this relationship to two main factors: the decreased efficacy of the human body’s synthesis of vitD through UVB-irradiation with age and the loss of mobility in the elderly that commonly restricts solar exposure( 30 , 31 ). Furthermore, the surprising prevalence of vitD deficiency among subjects younger than 50 years of age is too drastic to be ignored. The proposed hypothesis for why vitD concentrations seem to be decreasing in the younger population would be the exponential increase in technology use such as big-screen television, phones and social media making them inclined to stay indoors as compared to prior generations and, consequently, having less sunlight exposure ( 32 ). As for the negative correlation between increased BMI and vitD status, it appears that vitD may well be sequestered in fat stores, reducing its bioavailability. This relationship between obesity and vitD deficiency is a consistent association found in published literature ( 15 , 29 , 33 ), with body fat content being inversely correlated to serum 25(OH)D concentrations. Lui et al. reported that obese adults showed 3.09 times higher prevalence of VitD deficiency (25(OH)D < 20 ng/mL) and 1.80 times higher prevalence of VitD insufficiency (25(OH)D < 30 ng/mL) than non-obese adults ( 28 ). A significant and positive correlation between vitD concentrations and albumin concentrations (r = 0.135, p = 0.007) was noted. This may be explained by the fact that about 90% of total vitD is bound with DBP while 10–15% is loosely bound with albumin, and about 0.1% is present as free-circulating fraction. Therefore, the availability of 25(OH)D depends not only on the total 25(OH)D concentration but also on the concentration of DBP and albumin ( 34 ). The negative correlation between vitD and PTH found in our study was consistent with previous findings ( 29 , 35 ). PTH and VitD are two major regulators of mineral metabolism. They play critical roles in the maintenance of calcium and phosphate homeostasis as well as the development and maintenance of bone health. PTH and VitD form a tightly controlled feedback cycle, PTH being a major stimulator of vitD synthesis in the kidney while vitD exerts negative feedback on PTH secretion. ( 35 , 36 ) As for the linear regression analysis, it identified five variables as independent and statistically significant predictors for vitD deficiency: older age, cold season, sunscreen use, number of variant alleles of CYP24A1-rs6013897and VDR-rs2228570. Serum 25(OH)D concentrations were, as expected, higher during hot season. It has been shown that during cold season serum 25(OH)D concentrations decline and VitD status typically reaches its nadir ( 29 ). Similarly, Levis et al. found that seasonal variation can lead to a 13–14% increase in serum 25(OH)D in summer compared to winter ( 37 ). In 4149 participants of the population-based Heinz Nixdorf Recall study, the prevalence of vitD deficiency rose to 92% in February/March whereas in June/July it decreased to 71% ( 38 ). On the other hand, another concern has been expressed due to the widespread use of sunscreens, particularly those with high SPF, leading to a significant decrease in solar induced previtamin D3 in the skin. Sunscreen absorbs UV-B and some UV-A light and prevents it from reaching and entering the skin. In the study of Holick et al. they found that a sunscreen with an SPF of eight can decrease vitD synthetic capacity by 95%, and SPF 15 can decrease it by 98% ( 39 ). Norval et al. also confirm that sunscreens can significantly reduce the production of vitD under strict photoprotection ( 40 ). In general, demographic, anthropometric and lifestyle factors are robust predictors of poor vitD status worldwide. However, advances in the genetics of vitD metabolism can provide another route to interpret the underlying cause of vitD deficiency. In the present study, two SNPs (rs6013897and rs2228570) have shown to be significantly involved in determining vitD status. The CYP24A1-rs6013897 was associated with serum 25(OH)D concentrations, as the risk for vitD deficiency increased by 2.89 (IC95%: 1.15–7.3; p = 0.024) for this SNP variant alleles. CYP24A1 is the cytochrome P450 component of the 25-hydroxyvitamin D-24-hydroxylase enzyme that catalyzes the conversion of 25(OH)D and 1,25(OH)2D into the less active 24-hydroxylated products, which lead to inadequate vitD status due to degradation of active vitD( 41 ). It plays a pivotal role in maintaining vitD homeostasis. In fact, deletion of Cyp24a1 in mice causes 1,25(OH)2D excess and hypercalcemia with severe bone mineralization defects and ectopic vascular calcification (renal calcium deposition) after chronic treatment with 1,25(OH) 2 D( 42 ). Several studies have explored the association of the rs6013897 polymorphisms, located in the intergenic region downstream of CYP24A1, with various diseases ( 43 , 44 ). Multivariate analysis in the study of Vidigal et al. showed that the CYP24A1-rs6013897 polymorphism was associated with a 3.5 times higher risk of colorectal cancer (95% CI 1.49–8.21; p = 0.004) in patients with the AT genotype and a 3.33-times higher risk in patients with the AT + AA genotype compared with patients having the TT genotype( 43 ). Another study stipulated that women with rs6013897 variant were more likely (86%) to develop breast cancer( 44 ). Despite the important role of CYP24A1 in vitD metabolism, the precise mechanism linking rs6013897 to vitD deficiency remains unclear. It has long been acknowledged that vitD was mainly effective by stimulation of its receptor (VDR). The responsiveness of VDR may be affected by gene polymorphisms, such as rs2228570. In our study, we noted that subjects with risk alleles of the this variant had an increased chance of presenting with a 25(OH)D concentration lower than 30 ng/ml [OR 2.89 (IC95%: 1.15–7.3; p = 0.024)]. The identified association between rs2228570 variants and 25(OH)D concentration observed in our study was consistent with previous findings( 45 , 46 ) ( 47 ). Individuals carrying the rs2228570 variant were shown to have significantly low concentrations of serum vitD when compared to the individuals with the common genotype in the Turkish Cypriot population (p = 0.023)( 47 ). Likely, a study in Chinese Han population indicated that individuals with the rs2228570 variant GG genotype had significantly lower serum 25-(OH)D concentrations in comparison to the AG and AA genotypes (p < 0.001) ( 46 ). The VDR gene spans 63.49 kb on the 12q12-q14 in the human genome. The minor allele of VDR SNP rs2228570 leads to a VDR protein with three amino acid longer through directly introducing a new translation start codon( 45 ). It is thought that the variation results in reduced stability and activity of the VDR protein, which alters the binding energy of its ligand ( 46 ). Therefore, carriers of the AA VDR genotype are expected to have more VDR activity than carriers of the AG or GG variants which attribute a significant role to the VDR gene and the rs2228570 G allele in reducing vitamin D concentrations. rs2228570 variant allele was also shown to be associated with several cases including increased risk of multiple sclerosis( 45 ), dyslipidemia( 46 ), gastric cancer( 48 ) and type 2 diabetes mellitus( 49 ). Due to the rising awareness of its beneficial effect on general health, there has been a marked increase in healthcare expenditure on vitD tests and prescriptions. Therefore, in order to decrease the costs of laboratory tests and the number of people who unnecessarily use vitD supplements, it would be useful to have a model to predict vitD deficiency reliably without the need to determine 25(OH)D initial status. This study showed that serum 25(OH)D concentrations could be predicted accurately by easily assessable predictors using two prediction models: the first included genetic and non genetic factors inducing vitD deficiency (CA) while the second included only non genetic factors (SA) which can easily be used in daily practice. Other than the predictors identified by the linear regression analysis, our predictive models have also identified dark phototype as a strong predictor for vitD deficiency. Unsurprisingly, skin color influenced 25(OH)D concentrations as dark skinned subjects had had a higher chance of hypovitaminosis D. Same results were observed in the ERICA survey, as nonwhites had a 55% higher proportional odds ratio of hypovitaminosis D than whites ( 4 ). Other studies have also found an association between darker skin color and low vitD concentrations ( 33 ) ( 28 ). It has long been proven that dark skinned subjects have natural sun protection and require at least three to five times longer sun exposure to produce the same amount of vitD as a white skinned subject ( 50 ), due to the melanin in the skin that slows down cutaneous production of vitD by absorbing most of the available UV light required for vitamin D synthesis ( 39 , 50 ). Phototype, season, sun screen use, and age are the most important predictors of vitD deficiency. They explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR- rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, the set of predictors included in the final model explained about 27.6% of the total variability in 25(OH)D concentrations. The unexplained remaining variability can be attributed to a variety of factors such as memory biases, errors regarding evaluation of sun exposure time and frequency or simply other complex biological parameters. The SA on the other hand, identified similar predictors for vitD deficiency but the predictive value was not as precise as the one determined by the CA which highlights the importance of genetic studies and its influence on variations in 25(OH)D concentrations. In both algorithms, the foremost role of sunlight exposure (skin phototype + season + sun screen use) in predicting vitD concentrations underscores the importance of incorporating this data into the creation of vitD screening tools in forthcoming endeavors. Likewise, the inclusion of age may enhance the accuracy of the developed equation. Our analyses showed that inclusion of characteristics that would require a time-consuming, costly and/or invasive assessment, such as genetic polymorphisms, did not improve substantially the discriminatory performance of the algorithm. Vignali et al. developed an algorithm using a simple questionnaire based on sunlight exposure and conducted on adult subjects living in a mountain village in Southern Italy. The algorithm was able to explain about 55.7% of the variance in serum 25(OH)D concentration. The best predictors were seasonality, daily sunlight exposure, and beach holidays in the past 12 months, which accounted for 27.9, 13.5, and 6.4% of the explained variance in the prediction of vitD status, respectively( 51 ). In the retrospective analysis of Lee et al. the multiple regression model on the determinants of 25(OH)D concentration described 14% of the total variance, with the greatest relative contribution from sun exposure (60%) while demographic (gender and age) contributed least (8%) to the explained variance ( 52 ). In the 2023 polish guidelines for Preventing and Treating Vitamin D Deficiency, authors stated that the use of cholecalciferol should be individualized depending on age, body weight, the sun exposure of an individual, dietary habits and lifestyle ( 11 ). Notably, the majority of the study subjects were oblivious of their vitD status when identified as deficient. Hence, the public needs to be educated about proper screening, prevention, and potential health complications due to vitD deficiency. In addition, subjects’ non-compliance or taking inappropriate dosage of a vitD supplement may as well lead to continuous deficiency. The main strength of the study is its being the first study reporting serum 25(OH)D concentrations in a more or less large number of healthy Tunisian adults. The questionnaire used to assess risk factors was simple, thorough and contained many of the known risk factors for vitD deficiency. We tested a wide range of demographic, lifestyle, anthropometric, and genetic factors in association with plasma 25OHD concentration which increased the predictive value of the algorithm and were thus able to identify determinants of vitD deficiency. Such knowledge could serve as a basis for the implementation of strategies to identify individuals at high risk for vitD deficiency and better target those in need for supplementation. However, we acknowledge that the developed algorithms have some limitations: the moderate sample size and the fact that the study population consisted only of adults living in the same geographical area (city of Sousse-Tunisia) so we cannot ensure that our sample was representative of the population as it was based on subjects from health care personnel at the hospital who may be different from general population. Therefore, it is imperative to validate the predictive efficacy of the existing algorithms across varied experimental settings and on a more extensive, representative sample of participants. Consequently, a broader study will be conducted across diverse latitudes and cities in Tunisia. Additionally, the incorporation of artificial intelligence and machine learning tools will be pursued to further enhance analysis and insights. While a blood test may remain the best way to measure serum 25(OH)D concentration, having the results regarding vitD status from a simple, reliable screening tool may negate the need for a blood test, and indeed could provide clinicians with the possibility to stratify individuals at heightened risk for vitD deficiency and enable them make well informed decisions regarding the necessity for supplementation to manage this deficiency. Declarations Acknowledgement We thank the Tunisian Ministry of Higher Education, Scientific Research and Technology and the Ministry of Health for their support. The authors are thankful to all the members of the biochemistry laboratory of Sahloul University Hospital for their cooperation in conducting this research specially Mrs. Henda Falfoul and Mr. Mahmoud Smida. Finally, we gratefully acknowledge the contribution of participating individuals whose cooperation made this study possible. Author Contribution Statement This study was supervised by Pr. Asma Omezzine and Pr. Ali Bouslama. Mariem Ammar, designed the study and was responsible for screening potentially eligible studies. Mariem Ammar, Syrine Heni and Sahbi Tira recruited the patients under the supervision of Dr. Sonia Ksibi. Mariem Ammar and Amani abderrahmane performed the genotyping. Pr. Asma Omezzine and Mariem Ammar performed the statistical analysis and the interpretation of the data. Mariem Ammar wrote the manuscript. Amira Moussa, Yassine Khalij and Haithem Hamdouni contributed in writing the manuscript. Pr. Asma Omezzine and Pr. Ali Bouslama provided a critical review of the manuscript. All authors read and approved the final manuscript. Data Availability Statement: The data supporting the findings of this study are available from the biochemistry department LR12SP11 research laboratory at Sahloul University Hospital in Sousse, Tunisia, upon reasonable request. Access to these data is subject to restrictions under the licensing agreement governing their use in this study. Requests for data should be addressed to the corresponding author, Mariem Ammar. Competing Interests Statement The authors have nothing to disclose. Funding This study was funded by grants from the Tunisian Ministry of Higher Education, Scientific Research and Technology and the Ministry of Health. References Holick MF, Binkley NC, Bischoff-Ferrari HA, Gordon CM, Hanley DA, Heaney RP, et al. 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Martins JS, Palhares MO, Teixeira OCM, Gontijo Ramos M. Vitamin D status and its association with parathyroid hormone concentration in Brazilians. Journal of nutrition and metabolism. 2017;2017. Jacquillet G, Unwin RJ. Physiological regulation of phosphate by vitamin D, parathyroid hormone (PTH) and phosphate (Pi). Pflügers Archiv-European J Physiol. 2019;471(1):83–98. Levis S, Gomez A, Jimenez C, Veras L, Ma F, Lai S, et al. Vitamin D deficiency and seasonal variation in an adult South Florida population. J Clin Endocrinol Metabolism. 2005;90(3):1557–62. Schramm S, Lahner H, Jöckel K-H, Erbel R, Führer D, Moebus S, et al. Impact of season and different vitamin D thresholds on prevalence of vitamin D deficiency in epidemiological cohorts—a note of caution. Endocrine. 2017;56(3):658–66. Holick MF. Sunlight and vitamin D for bone health and prevention of autoimmune diseases, cancers, and cardiovascular disease. Am J Clin Nutr. 2004;80(6):S1678–88. Norval M, Wulf HC. Does chronic sunscreen use reduce vitamin D production to insufficient levels? Br J Dermatol. 2009;161(4):732–6. Veldurthy V, Wei R, Campbell M, Lupicki K, Dhawan P, Christakos S. 25-Hydroxyvitamin D3 24-hydroxylase: a key regulator of 1, 25 (OH) 2D3 catabolism and calcium homeostasis. Vitamins Horm. 2016;100:137–50. St-Arnaud R, Arabian A, Travers R, Barletta F, Raval-Pandya M, Chapin K, et al. Deficient mineralization of intramembranous bone in vitamin D-24-hydroxylase-ablated mice is due to elevated 1, 25-dihydroxyvitamin D and not to the absence of 24, 25-dihydroxyvitamin D. Endocrinology. 2000;141(7):2658–66. Vidigal VM, Silva TD, de Oliveira J, Pimenta CAM, Felipe AV, Forones NM. Genetic polymorphisms of vitamin D receptor (VDR), CYP27B1 and CYP24A1 genes and the risk of colorectal cancer. Int J Biol Mark. 2017;32(2):224–30. Jorde R, Schirmer H, Wilsgaard T, Joakimsen RM, Mathiesen EB, Njølstad I, et al. Polymorphisms related to the serum 25-hydroxyvitamin D level and risk of myocardial infarction, diabetes, cancer and mortality. The Tromsø Study. PLoS ONE. 2012;7(5):e37295. Smolders J, Damoiseaux J, Menheere P, Tervaert JWC, Hupperts R. Fok-I vitamin D receptor gene polymorphism (rs10735810) and vitamin D metabolism in multiple sclerosis. J Neuroimmunol. 2009;207(1–2):117–21. Jia J, Tang Y, Shen C, Zhang N, Ding H, Zhan Y. Vitamin D receptor polymorphism rs2228570 is significantly associated with risk of dyslipidemia and serum LDL levels in Chinese Han population. Lipids Health Dis. 2018;17(1):1–8. Tuncel G, Temel SG, Ergoren MC. Strong association between VDR FokI (rs2228570) gene variant and serum vitamin D levels in Turkish Cypriots. Mol Biol Rep. 2019;46(3):3349–55. Hoseinkhani Z, Rastegari-Pouyani M, Tajemiri F, Yari K, Mansouri K. Association of Vitamin D Receptor Polymorphisms (FokI (Rs2228570), ApaI (Rs7975232), BsmI (Rs1544410), and TaqI (Rs731236)) with Gastric Cancer in a Kurdish Population from West of Iran. Rep Biochem Mol Biology. 2021;9(4):435. Kaftan AN, Hussain MK, Algenabi AHA, Omara AM, Al-Kashwan TA. Association of sunshine vitamin receptor gene polymorphisms (rs 2228570) and (rs7975232) with the type 2 diabetes mellitus in Iraqi patients from the middle Euphrates region. Gene Rep. 2021;22:100977. Clemens T, Henderson S, Adams J, Holick M. Increased skin pigment reduces the capacity of skin to synthesise vitamin D3. Lancet. 1982;319(8263):74–6. Vignali E, Macchia E, Cetani F, Reggiardo G, Cianferotti L, Saponaro F, et al. Development of an algorithm to predict serum vitamin D levels using a simple questionnaire based on sunlight exposure. Endocrine. 2017;55(1):85–92. Lee A, Samy W, Chiu CH, Chan SKC, Gin T, Chui PT. Determinants of serum 25-hydroxyvitamin D concentrations and a screening test for moderate-to-severe hypovitaminosis D in Chinese patients undergoing total joint arthroplasty. J Arthroplast. 2016;31(9):1921–6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 21 May, 2024 Submission checks completed at journal 21 May, 2024 First submitted to journal 20 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4448996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305270819,"identity":"6ee55363-d136-4e49-aa7a-f151c4c8a4cc","order_by":0,"name":"Mariem AMMAR","email":"data:image/png;base64,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","orcid":"","institution":"Biochemistry Department, LR12SP11, Sahloul University Hospital.","correspondingAuthor":true,"prefix":"","firstName":"Mariem","middleName":"","lastName":"AMMAR","suffix":""},{"id":305270820,"identity":"585a85a4-641a-4e10-bdae-842adfab50d5","order_by":1,"name":"Amani ABDERRAHMANE","email":"","orcid":"","institution":"Biochemistry Department, LR12SP11, Sahloul University Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Amani","middleName":"","lastName":"ABDERRAHMANE","suffix":""},{"id":305270821,"identity":"87b73b2c-588e-4b9e-94b6-ce765e18692a","order_by":2,"name":"Syrine HENI","email":"","orcid":"","institution":"faculty of pharmacy of Monastir","correspondingAuthor":false,"prefix":"","firstName":"Syrine","middleName":"","lastName":"HENI","suffix":""},{"id":305270822,"identity":"10797a4c-4ecd-4b7d-a950-698cc37f082c","order_by":3,"name":"Mohamed Sahbi TIRA","email":"","orcid":"","institution":"faculty of pharmacy of Monastir","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Sahbi","lastName":"TIRA","suffix":""},{"id":305270823,"identity":"39ab622c-c9cf-4ab0-ad99-e8c7a420c205","order_by":4,"name":"Amira Moussa","email":"","orcid":"","institution":"Biochemistry Department, LR12SP11, Sahloul University Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Amira","middleName":"","lastName":"Moussa","suffix":""},{"id":305270824,"identity":"17788847-c3ce-45d8-b700-3eaa8fcbf10b","order_by":5,"name":"Yassine KHALIJ","email":"","orcid":"","institution":"Biochemistry Department, LR12SP11, Sahloul University Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Yassine","middleName":"","lastName":"KHALIJ","suffix":""},{"id":305270825,"identity":"2908f60b-86df-4380-ae44-359e5f088967","order_by":6,"name":"Sonia Ksibi","email":"","orcid":"","institution":"Occupational Health Service, Sahloul University Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Sonia","middleName":"","lastName":"Ksibi","suffix":""},{"id":305270826,"identity":"8e0a88b7-8683-432d-9ce7-59068f54c413","order_by":7,"name":"Ali Bouslama","email":"","orcid":"","institution":"Biochemistry Department, LR12SP11, Sahloul University Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Bouslama","suffix":""},{"id":305270827,"identity":"ac149149-7972-4bcc-9eab-03ee834c578d","order_by":8,"name":"Asma Omezzine","email":"","orcid":"","institution":"Biochemistry Department, LR12SP11, Sahloul University Hospital.","correspondingAuthor":false,"prefix":"","firstName":"Asma","middleName":"","lastName":"Omezzine","suffix":""}],"badges":[],"createdAt":"2024-05-20 12:06:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4448996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4448996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57640992,"identity":"d1d4b0b6-58e7-4078-b992-71f88753204b","added_by":"auto","created_at":"2024-06-03 17:21:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":639681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4448996/v1/b7e5af87-5d60-4588-b48f-1a609bb191a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction algorithms using genetic and non genetic factors inducing vitamin D deficiency among healthy adults","fulltext":[{"header":"Introduction/Background","content":"\u003cp\u003eVitD is a fat soluble sunshine vitamin that plays an essential role in sustaining the skeletal integrity by maintaining phosphate and calcium homeostasis and regulating bone metabolism (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The global prevalence of vitD deficiency appears to be increasing, and it has been linked to skeletal abnormalities, rickets and growth problems in children, and to osteomalacia, osteopenia, osteoporosis and skeletal fractures in adults. It is also associated with extra-skeletal diseases including autoimmunity, cancer, respiratory diseases, neurologic disorders and other adverse outcomes(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHypovitaminosis D has become a pandemic with a myriad of health consequences and is being observed in all ethnicities and age groups worldwide(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Great controversy exists with regards to the optimal serum 25OHD concentration for bone health (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Some guidelines, including the Institute of Medicine (IOM) guidelines reported little or no additional benefit with concentrations\u0026thinsp;\u0026gt;\u0026thinsp;20 ng/ml(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, a concentration of 30 ng/ml or greater was recommended by the Endocrine Society(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), the Osteoporosis Research and Information Group(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the National Osteoporosis Society(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) and the international osteoporosis foundation(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dietary source of vitD is poor and approximately 90% of its supply originates from sunshine-induced skin synthesis(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). After exposure to UVB rays (290\u0026ndash;320 nm) 7-dehydrocholesterol (7-DHC) in the skin epidermis is isomerized to pre-vitamin D3 (cholecalciferol) which undergoes two hydroxylations by cytochrome P450 enzymes. The first one occurs in the liver by 25-hydroxylase enzyme, producing 25(OH)D, the main circulating form of vitD. The next hydroxylation occurs in the distal tubules of the kidney by 1-hydroxylase enzyme producing the biologically active 1, 25-dihydroxyvitamin D (1,25(OH)\u003csub\u003e2\u003c/sub\u003eD). Both 25(OH)D and 1,25(OH)\u003csub\u003e2\u003c/sub\u003eD can be inactivated after being hydroxylated at C24 by cytochrome P450 enzyme (CYP24A1). The majority of 25(OH)D and 1,25(OH)\u003csub\u003e2\u003c/sub\u003eD circulate in plasma bound to specific transport proteins called vitD binding protein (DBP) while a small fraction is free. The circulating concentration of vitD is tightly regulated and acts through a specific receptor (VDR) to mediate its genomics actions.(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe variability in vitamin D status and the severity of its deficiency are likely to be multifaceted, depending on various genetic, environmental and personal factors (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Environmental factors, such as ethnicity, latitude, sun exposure and season, determine whether there is sufficient UVB radiation to stimulate dermal vitD synthesis(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Personal factors including gender, age, skin pigmentation, use of high SPF (sun protection factor), physical activity, clothing habits and dietary habits can influence individual vitD status(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Great interest has now turned to the gene-environment interactions that could have an impact on vitD bioavaibility and therefore a clear understanding of the genetic factors involved in determining vitD status is necessary to appreciate the possible gene-environment interactions of vitD. (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn Tunisia, very few studies were designed to investigate the prevalence of hypovitaminosis D and most reported data were from studies in correlation with vitD deficiency related diseases in rather small number of patients. The objectives of this study were (i) to assess the prevalence of hypovitaminosis D, (ii) to define its associated risk factors among healthy adults, and (iii) to develop algorithms assessing individual vitD status.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects and Study design\u003c/h2\u003e \u003cp\u003e The study was carried out in Sahloul University Hospital Center where we recruited a cohort of 394 health care personnel who were free from any condition affecting bone health, general nutrition and growth. Subjects with any chronic illness involving the liver and kidney or causing malabsorption, who used steroids, anticonvulsants, or vitD treatment, multivitamins or any medication that might affect calcium and vitD metabolism, were excluded. All individuals were asked to complete a generalized questionnaire that was based on the study by Berquist et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), it included present and past medical history, medical use, pain during walking, recruitment season (Mars-April-May: Spring; June-July-August: Summer; September-October-November: Autumn and December-jannuary-february: Winter), skin pigmentation which was assessed using a 6 -point Fitzpatrick scale (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) (Light-skinned individuals were characterized as skin type I,II, III and dark-skinned individuals were classified as skin type IV, V, VI) age, gender, physical activity, clothing habits, housing conditions, sun exposure frequency, parts of the body that are typically exposed to sunlight (head, face, hands and other parts of the body) and frequency of sunscreen use. A validated food-frequency questionnaire was used in order to determine dietary habits and vitD intake (participants were asked to keep non-consecutive 3-day food intake record including one weekend day or holiday, portion sizes and preparation methods were assessed. After converting each food item into weight (g), the corresponding nutritional composition was obtained from standard food composition tables, and each participant's total dietary intake of macro- and micronutrients was assessed by Di\u0026eacute;t\u0026eacute;tique 5.3(free online software). All participants submitted written informed consents before inclusion. All methods of sampling and protocols were approved by the Ethics Committee of Sahloul University Hospital and were performed in accordance with the relevant guidelines of the Helsinki Declaration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDemographic, anthropometric Parameters and Biochemical analysis\u003c/h2\u003e \u003cp\u003eDemographic variables included age and gender. Participants were categorized by age into young adults (\u0026lt;\u0026thinsp;50years) and older adults (\u0026gt;\u0026thinsp;50 years). Anthropometrics parameters included weight, height and Body mass index (BMI: kg/m\u003csup\u003e2\u003c/sup\u003e). Overweight was defined as BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e. Biochemical factors including fasting glucose, serum calcium, phosphate, sodium, potassium, urea, creatinine, albumin, aspartate transaminase (ASAT), Alanine transaminase (ALAT) alkaline phosphatase (PAL), total cholesterol (TC), triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C) were all measured with the use of an automatic biochemistry analyzer (Beckmann Coulter, Fullerton, CA, USA). Low density lipoprotein cholesterol (LDL-C) was estimated using Friedewald formula (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Parathyroid hormone (PTH) and insulin fasting concentrations were measured with electrochemiluminescence immunoassay on COBAS e411 (Roche Diagnostics GmbH, Mannheim, Germany). HbA1c was measured by HPLC (VARIANT II BIO-RAD, Hercules, CA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of circulating 25(OH)D concentration\u003c/h2\u003e \u003cp\u003eSerum 25(OH)D was quantified by an automated chemiluminescent immunoassay performed on COBAS e411 (Roche Diagnostics GmbH, Mannheim, Germany). According to the manufacturer\u0026rsquo;s specifcations, the intra- and inter-assay coefficients of variation were 5.4% and 8.4%, respectively with a functional sensitivity of 3 ng/mL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eGenetic analysis\u003c/h2\u003e \u003cp\u003eDNA extraction was performed using salting-out DNA extraction protocol(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Genotype analysis was carried out by Polymerase Chain Reaction (PCR) using a PROFLEX PCR System (Applied Biosystems by Life Technologies) followed by Restriction Fragment Length Polymorphism (RFLP) assay.\u003c/p\u003e \u003cp\u003eFifteen candidate SNPs from six vitD pathway genes were selected from the International HapMap Project according to the following criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) biological importance in vitD metabolism, transportation, or degradation; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) evidence of a significant association in previous GWASs and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) on minor allele frequencies (MAF\u0026thinsp;\u0026gt;\u0026thinsp;10%). The selected SNPs were: [DBP (rs4588, rs7041), CYP2R1 (rs2060793, rs10766197, rs12794714, rs10741657), CYP27B1 (rs10877012), CYP24A1 (rs6013897), DHCR7/NADSYN (rs3794060, rs12785878, rs3829251) and VDR (rs1544410, rs731236, rs7975232, rs2228570)].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was performed using the SPSS v23 software. The Hardy-Weinberg equilibrium was verified by the chi-square test. The quantitative results were compared using ANOVA test, Student test, or Mann-Whitney test if not Gaussian distribution. The qualitative variables, expressed in frequency (N) and in percentage (%), have been compared by the chi-square test and their association with vitD status were evaluated by Odds ratio (OR) with 95% confidence intervals (CI) and adjusted for potential confounding factors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.25) by binary logistic regression. The coefficient of correlation (r) was determined. To further evaluate the joint associations of demographic, lifestyle and genetic factors with the risk of vitD deficiency, binary multivariate logistic regression with stepwise backward selection method was used to arrive at the final model including only the most associated factors with vitD deficiency.\u003c/p\u003e \u003cp\u003eTo establish the predictive algorithms, we defined a randomly selected 80% derivation cohort and a validation test cohort made up of the remaining 20% of the total study population. A multiple linear regression was applied using the enter method to generate the algorithms, that would be easier to use in routine clinical practice (simple equation). We have considered 25 (OH)D concentration as a dependent variable and all variables showing significant association (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) as independent variables to be included in the composite algorithm (CA) that included genetic and non genetic factors. The simple algorithm (SA) was defined including only subject\u0026rsquo;s sun exposure degree and demographic variables. The performance of these algorithms was evaluated by calculating the coefficient of determination (intercept) and the standardized regression coefficient (Beta) to estimate the variability explained by each model.\u003c/p\u003e \u003cp\u003ePredicted mean 25(OH)D concentration\u0026thinsp;=\u0026thinsp;Intercept + \u0026sum; Beta (variable) \u0026times; value (variable).\u003c/p\u003e \u003cp\u003eWe calculated afterwards a root mean square prediction error (MPE) to quantify the average absolute difference between observed and predicted serum 25(OH)D concentrations. The predicted concentration was estimated according to (SA and CA) and then compared to the initial 25(OH)D concentration. For all statistical tests mentioned above, a p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of participants and prevalence of VitD deficiency\u003c/h2\u003e \u003cp\u003eAmong the studied subjects, 70.1% were female, 33.2% were aged above 50 years, 20% were considered as obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30) and 65.2% of the participants had a dark-colored skin (type IV to VI).\u003c/p\u003e \u003cp\u003eThe present study showed a remarkably high prevalence of vitD deficiency among health care personnel. 92.1% were vitD deficient (25(OH)D\u0026thinsp;\u0026lt;\u0026thinsp;30ng/ml) with a mean concentration of 14,68\u0026thinsp;\u0026plusmn;\u0026thinsp;7,05ng/ml vs 39,46\u0026thinsp;\u0026plusmn;\u0026thinsp;11,87ng/ml for non deficient subjects (25(OH)D\u0026thinsp;\u0026gt;\u0026thinsp;30ng/ml). We observed statistically significant differences in vitD deficiency rates with respect to gender, age and BMI.\u003c/p\u003e \u003cp\u003e25(OH)D concentrations were significantly lower among women than men (12.90[0-76.40] vs 22.20[5.3\u0026ndash;52.5]ng/ml; p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The median 25(OH)D value was lowest among overweighed (11.60 [0\u0026ndash;57] vs 15.50 [0-76.40]ng/ml; p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001), older subjects (13.10 [0-65.70] vs 16.20 [0-76.40]ng/ml; p\u0026thinsp;=\u0026thinsp;0.003) and during cold season (13.70 [0-58.10]vs 16.30 [3.50\u0026ndash;76.40] ng/ml; p\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the main characteristics of the study participants and the distribution of vitD status according to the studied parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of vitamin D status according to epidemiological and anthropometric characteristics *p\u0026thinsp;\u0026lt;\u0026thinsp;0,05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVitamin D (ng/ml)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation\u003c/p\u003e \u003cp\u003eor Median [min-max]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDeficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.46\u0026thinsp;\u0026plusmn;\u0026thinsp;11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon Deficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.20 [5.3\u0026ndash;52.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.90 [0-76.40]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.20 [0-76.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.10 [0-65.70]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.50 [0-76.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30 kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.60 [0\u0026ndash;57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot season (Spring\u0026thinsp;+\u0026thinsp;Summer)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.30 [3.50\u0026ndash;76.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold season (Automn\u0026thinsp;+\u0026thinsp;Winter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.70 [0-58.10]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFactors associated with 25(OH)D concentration\u003c/h2\u003e \u003cp\u003eIn simple regression analysis VitD concentration correlated positively with albumin (r\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.007) and negatively with serum PTH (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.303, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.198, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and BMI (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003eUnivariate analyses showed significant associations between vitD status and season, gender, age, BMI, sun screen use, phototype, veil, covering clothing, physical activity and albumin. All these factors fulfilled the criteria p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and were considered as potential confounding factors for genotypic analyses.\u003c/p\u003e \u003cp\u003eGenotype associations with VitD deficiency were adjusted to the fore-mentioned confounding factors so that we only obtain the specific effect of the studied SNPs. According to dominant model and after adjustment by binary logistic regression, we noted that there was a significant association of vitD deficiency according to genotype for two SNPs: CYP24A1-rs6013897 and VDR-rs2228570. The results revealed that OR of vitD deficiency associated with homozygote variant genotypes were 3.16 (IC95%: 1.7\u0026ndash;5.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)] and 2.89 (IC95%: 1.15\u0026ndash;7.3; p\u0026thinsp;=\u0026thinsp;0.024)] respectively. These associations were significant for the heterozygote genotypes as well. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVitD deficiency according to SNPs genotypes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon Deficient\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeficient\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecrudeOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIC 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOR ƚ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIC 95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers2228570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (41,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (7,4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (32,3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147 (40,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[2.88\u0026ndash;17.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[1.53\u0026ndash;5.28]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (25,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e189 (52,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[4.31\u0026ndash;29.96]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[1.7\u0026ndash;5.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ers6013897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (54,8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 (28,1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003e0.008*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (35,5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e215 (59,2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.47\u0026ndash;7.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[1.15\u0026ndash;7.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.024*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (9,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (12,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[1.01\u0026ndash;9.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.039*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e[1.03\u0026ndash;3.41]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.049*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eƚ OR of vitD deficiency associated to genotypes and adjusted for potential confounders (season, gender, age, BMI, sun screen use, phototype, veil, covering clothing, physical activity and albumin.); *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo assess the co-influence of non genetic and genetic risk factors on vitD status, we carried out multivariate analyses by backward conditional binary logistic regression where season, sun screen use, age, VDR-rs2228570 and CYP24A-rs6013897 were retained as the most significant factors associated with vitD deficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrediction algorithms\u003c/h2\u003e \u003cp\u003eTo establish the predictive algorithms, we defined (CA): a composite algorithm that included genetic and non genetic factors and (SA): a simple algorithm including only environmental non genetic factors. Multiple linear regression analysis resulted in the following algorithms of predictive 25(OH)D concentration:\u003c/p\u003e \u003cp\u003e \u003cb\u003eCA Predicted 25(OH)D concentration\u003c/b\u003e\u0026thinsp;=\u0026thinsp;35.381 \u0026ndash; (\u0026ldquo;number of rs6013897 variant allele(s)\u0026rdquo; \u0026times; 5.826) \u0026ndash; (\u0026ldquo;number of rs2228570 variant allele(s)\u0026rdquo; \u0026times; 6.265) \u0026ndash; (\u0026lsquo;\u0026lsquo;dark phototype\u0026rsquo;\u0026rsquo; \u0026times; 6.349) \u0026ndash; (\u0026ldquo;sun screen use\u0026rdquo; \u0026times; 3.584) \u0026ndash; (\u0026ldquo;cold season\u0026rdquo; \u0026times; 2.939) \u0026ndash; (\u0026ldquo;age (years)\u0026rdquo; \u0026times; 2.150).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSA Predicted 25(OH)D concentration\u003c/b\u003e\u0026thinsp;=\u0026thinsp;31,412 \u0026ndash; (\u0026lsquo;\u0026lsquo;dark phototype\u0026rsquo;\u0026rsquo; \u0026times; 8,308) - (\u0026ldquo;sun screen use\u0026rdquo; \u0026times; 3,341) - (\u0026ldquo;cold season\u0026rdquo; \u0026times; 3,354) - (\u0026ldquo;age (years)\u0026rdquo; \u0026times; 0,123).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModel Performance\u003c/h2\u003e \u003cp\u003eIn order to evaluate the performance of our predictive algorithms, we calculated a root mean square prediction error (MPE) to quantify the average absolute difference between observed and predicted serum 25(OH)D concentrations according to the two predictive models.\u003c/p\u003e \u003cp\u003eThere was no significant difference between the observed 25(OH)D concentration and those predicted by the two algorithms (CA p\u0026thinsp;=\u0026thinsp;0.736 and SA p\u0026thinsp;=\u0026thinsp;0.481). Using the predictive algorithms, we also obtained low percentages of difference compared to the observed concentration (CA MPE = -8.2%; SA MPE\u0026thinsp;=\u0026thinsp;17.9%). Nevertheless, a marginal superiority in performance was evident for the CA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenetic and non genetic factors contribution on vitD status\u003c/h2\u003e \u003cp\u003eMultiple linear regression revealed that season, sun screen use, phototype and age explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR-rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, genetic and non genetic factors explained up to 27.6% of the variance in 25(OH)D concentrations. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic and non genetic factors contribution on vitD deficiency\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eCumulative R\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenetic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2228570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers6013897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eNon genetic factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhototype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSun screen use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn our previous study (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), we focused on evaluating the impact of specific polymorphisms within pivotal genes of the vitamin D metabolic pathway on the efficacy of supplementation. Our focus shifts in this study to investigate vitD basal status among healthy adults in Tunisia, while also exploring the myriad factors that contribute to variations in its concentration.\u003c/p\u003e \u003cp\u003eOur study showed that the prevalence of vitD deficiency state was up to 92.1% which was in line with results from other studies in healthy populations (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), including small studies of subgroups (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Tunisia is located at latitude 35\u0026deg;49\u0026rsquo;34\u0026rsquo;\u0026rsquo;N, longitude 10\u0026deg;38\u0026rsquo;24\u0026rsquo;\u0026rsquo;E and on an average elevation of 246 meters above sea level, with relatively constant sunlight even in wintertime.\u003c/p\u003e \u003cp\u003eIn the present study, we observed higher serum 25(OH)D concentrations in males than in females (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This can likely be explained by differences in lifestyle between men and women, particularly the longer time spent outdoors by men(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Additionally, women are at least three times more likely than men to use sunscreen daily and therefore have less skin exposure to the sun (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study showed that age was related to serum 25(OH)D concentration after adjustment for confounding factors. Similar results were observed in the study of Lui et al. where Serum 25(OH)D concentration were 3.7 nmol/l lower in old (\u0026ge;\u0026thinsp;60 years) compared with young adults(18\u0026ndash;59 years) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Prior studies have also shown that increasing age is commonly associated with an increased risk of vitD deficiency (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), attributing this relationship to two main factors: the decreased efficacy of the human body\u0026rsquo;s synthesis of vitD through UVB-irradiation with age and the loss of mobility in the elderly that commonly restricts solar exposure(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Furthermore, the surprising prevalence of vitD deficiency among subjects younger than 50 years of age is too drastic to be ignored. The proposed hypothesis for why vitD concentrations seem to be decreasing in the younger population would be the exponential increase in technology use such as big-screen television, phones and social media making them inclined to stay indoors as compared to prior generations and, consequently, having less sunlight exposure (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs for the negative correlation between increased BMI and vitD status, it appears that vitD may well be sequestered in fat stores, reducing its bioavailability. This relationship between obesity and vitD deficiency is a consistent association found in published literature (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), with body fat content being inversely correlated to serum 25(OH)D concentrations. Lui et al. reported that obese adults showed 3.09 times higher prevalence of VitD deficiency (25(OH)D\u0026thinsp;\u0026lt;\u0026thinsp;20 ng/mL) and 1.80 times higher prevalence of VitD insufficiency (25(OH)D\u0026thinsp;\u0026lt;\u0026thinsp;30 ng/mL) than non-obese adults (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA significant and positive correlation between vitD concentrations and albumin concentrations (r\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.007) was noted. This may be explained by the fact that about 90% of total vitD is bound with DBP while 10\u0026ndash;15% is loosely bound with albumin, and about 0.1% is present as free-circulating fraction. Therefore, the availability of 25(OH)D depends not only on the total 25(OH)D concentration but also on the concentration of DBP and albumin (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The negative correlation between vitD and PTH found in our study was consistent with previous findings (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). PTH and VitD are two major regulators of mineral metabolism. They play critical roles in the maintenance of calcium and phosphate homeostasis as well as the development and maintenance of bone health. PTH and VitD form a tightly controlled feedback cycle, PTH being a major stimulator of vitD synthesis in the kidney while vitD exerts negative feedback on PTH secretion. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAs for the linear regression analysis, it identified five variables as independent and statistically significant predictors for vitD deficiency: older age, cold season, sunscreen use, number of variant alleles of CYP24A1-rs6013897and VDR-rs2228570.\u003c/p\u003e \u003cp\u003eSerum 25(OH)D concentrations were, as expected, higher during hot season. It has been shown that during cold season serum 25(OH)D concentrations decline and VitD status typically reaches its nadir (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Similarly, Levis et al. found that seasonal variation can lead to a 13\u0026ndash;14% increase in serum 25(OH)D in summer compared to winter (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In 4149 participants of the population-based Heinz Nixdorf Recall study, the prevalence of vitD deficiency rose to 92% in February/March whereas in June/July it decreased to 71% (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, another concern has been expressed due to the widespread use of sunscreens, particularly those with high SPF, leading to a significant decrease in solar induced previtamin D3 in the skin. Sunscreen absorbs UV-B and some UV-A light and prevents it from reaching and entering the skin. In the study of Holick et al. they found that a sunscreen with an SPF of eight can decrease vitD synthetic capacity by 95%, and SPF 15 can decrease it by 98% (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Norval et al. also confirm that sunscreens can significantly reduce the production of vitD under strict photoprotection (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn general, demographic, anthropometric and lifestyle factors are robust predictors of poor vitD status worldwide. However, advances in the genetics of vitD metabolism can provide another route to interpret the underlying cause of vitD deficiency. In the present study, two SNPs (rs6013897and rs2228570) have shown to be significantly involved in determining vitD status.\u003c/p\u003e \u003cp\u003eThe CYP24A1-rs6013897 was associated with serum 25(OH)D concentrations, as the risk for vitD deficiency increased by 2.89 (IC95%: 1.15\u0026ndash;7.3; p\u0026thinsp;=\u0026thinsp;0.024) for this SNP variant alleles. CYP24A1 is the cytochrome P450 component of the 25-hydroxyvitamin D-24-hydroxylase enzyme that catalyzes the conversion of 25(OH)D and 1,25(OH)2D into the less active 24-hydroxylated products, which lead to inadequate vitD status due to degradation of active vitD(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). It plays a pivotal role in maintaining vitD homeostasis. In fact, deletion of Cyp24a1 in mice causes 1,25(OH)2D excess and hypercalcemia with severe bone mineralization defects and ectopic vascular calcification (renal calcium deposition) after chronic treatment with 1,25(OH)\u003csub\u003e2\u003c/sub\u003eD(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Several studies have explored the association of the rs6013897 polymorphisms, located in the intergenic region downstream of CYP24A1, with various diseases (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Multivariate analysis in the study of Vidigal et al. showed that the CYP24A1-rs6013897 polymorphism was associated with a 3.5 times higher risk of colorectal cancer (95% CI 1.49\u0026ndash;8.21; p\u0026thinsp;=\u0026thinsp;0.004) in patients with the AT genotype and a 3.33-times higher risk in patients with the AT\u0026thinsp;+\u0026thinsp;AA genotype compared with patients having the TT genotype(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Another study stipulated that women with rs6013897 variant were more likely (86%) to develop breast cancer(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Despite the important role of CYP24A1 in vitD metabolism, the precise mechanism linking rs6013897 to vitD deficiency remains unclear.\u003c/p\u003e \u003cp\u003eIt has long been acknowledged that vitD was mainly effective by stimulation of its receptor (VDR). The responsiveness of VDR may be affected by gene polymorphisms, such as rs2228570. In our study, we noted that subjects with risk alleles of the this variant had an increased chance of presenting with a 25(OH)D concentration lower than 30 ng/ml [OR 2.89 (IC95%: 1.15\u0026ndash;7.3; p\u0026thinsp;=\u0026thinsp;0.024)]. The identified association between rs2228570 variants and 25(OH)D concentration observed in our study was consistent with previous findings(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Individuals carrying the rs2228570 variant were shown to have significantly low concentrations of serum vitD when compared to the individuals with the common genotype in the Turkish Cypriot population (p\u0026thinsp;=\u0026thinsp;0.023)(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Likely, a study in Chinese Han population indicated that individuals with the rs2228570 variant GG genotype had significantly lower serum 25-(OH)D concentrations in comparison to the AG and AA genotypes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe VDR gene spans 63.49 kb on the 12q12-q14 in the human genome. The minor allele of VDR SNP rs2228570 leads to a VDR protein with three amino acid longer through directly introducing a new translation start codon(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). It is thought that the variation results in reduced stability and activity of the VDR protein, which alters the binding energy of its ligand (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Therefore, carriers of the AA VDR genotype are expected to have more VDR activity than carriers of the AG or GG variants which attribute a significant role to the VDR gene and the rs2228570 G allele in reducing vitamin D concentrations. rs2228570 variant allele was also shown to be associated with several cases including increased risk of multiple sclerosis(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), dyslipidemia(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e), gastric cancer(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) and type 2 diabetes mellitus(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDue to the rising awareness of its beneficial effect on general health, there has been a marked increase in healthcare expenditure on vitD tests and prescriptions. Therefore, in order to decrease the costs of laboratory tests and the number of people who unnecessarily use vitD supplements, it would be useful to have a model to predict vitD deficiency reliably without the need to determine 25(OH)D initial status. This study showed that serum 25(OH)D concentrations could be predicted accurately by easily assessable predictors using two prediction models: the first included genetic and non genetic factors inducing vitD deficiency (CA) while the second included only non genetic factors (SA) which can easily be used in daily practice.\u003c/p\u003e \u003cp\u003eOther than the predictors identified by the linear regression analysis, our predictive models have also identified dark phototype as a strong predictor for vitD deficiency. Unsurprisingly, skin color influenced 25(OH)D concentrations as dark skinned subjects had had a higher chance of hypovitaminosis D. Same results were observed in the ERICA survey, as nonwhites had a 55% higher proportional odds ratio of hypovitaminosis D than whites (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Other studies have also found an association between darker skin color and low vitD concentrations (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). It has long been proven that dark skinned subjects have natural sun protection and require at least three to five times longer sun exposure to produce the same amount of vitD as a white skinned subject (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e), due to the melanin in the skin that slows down cutaneous production of vitD by absorbing most of the available UV light required for vitamin D synthesis (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhototype, season, sun screen use, and age are the most important predictors of vitD deficiency. They explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR- rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, the set of predictors included in the final model explained about 27.6% of the total variability in 25(OH)D concentrations. The unexplained remaining variability can be attributed to a variety of factors such as memory biases, errors regarding evaluation of sun exposure time and frequency or simply other complex biological parameters.\u003c/p\u003e \u003cp\u003eThe SA on the other hand, identified similar predictors for vitD deficiency but the predictive value was not as precise as the one determined by the CA which highlights the importance of genetic studies and its influence on variations in 25(OH)D concentrations.\u003c/p\u003e \u003cp\u003eIn both algorithms, the foremost role of sunlight exposure (skin phototype\u0026thinsp;+\u0026thinsp;season\u0026thinsp;+\u0026thinsp;sun screen use) in predicting vitD concentrations underscores the importance of incorporating this data into the creation of vitD screening tools in forthcoming endeavors. Likewise, the inclusion of age may enhance the accuracy of the developed equation. Our analyses showed that inclusion of characteristics that would require a time-consuming, costly and/or invasive assessment, such as genetic polymorphisms, did not improve substantially the discriminatory performance of the algorithm.\u003c/p\u003e \u003cp\u003eVignali et al. developed an algorithm using a simple questionnaire based on sunlight exposure and conducted on adult subjects living in a mountain village in Southern Italy. The algorithm was able to explain about 55.7% of the variance in serum 25(OH)D concentration. The best predictors were seasonality, daily sunlight exposure, and beach holidays in the past 12 months, which accounted for 27.9, 13.5, and 6.4% of the explained variance in the prediction of vitD status, respectively(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). In the retrospective analysis of Lee et al. the multiple regression model on the determinants of 25(OH)D concentration described 14% of the total variance, with the greatest relative contribution from sun exposure (60%) while demographic (gender and age) contributed least (8%) to the explained variance (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the 2023 polish guidelines for Preventing and Treating Vitamin D Deficiency, authors stated that the use of cholecalciferol should be individualized depending on age, body weight, the sun exposure of an individual, dietary habits and lifestyle (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, the majority of the study subjects were oblivious of their vitD status when identified as deficient. Hence, the public needs to be educated about proper screening, prevention, and potential health complications due to vitD deficiency. In addition, subjects\u0026rsquo; non-compliance or taking inappropriate dosage of a vitD supplement may as well lead to continuous deficiency.\u003c/p\u003e \u003cp\u003eThe main strength of the study is its being the first study reporting serum 25(OH)D concentrations in a more or less large number of healthy Tunisian adults. The questionnaire used to assess risk factors was simple, thorough and contained many of the known risk factors for vitD deficiency. We tested a wide range of demographic, lifestyle, anthropometric, and genetic factors in association with plasma 25OHD concentration which increased the predictive value of the algorithm and were thus able to identify determinants of vitD deficiency. Such knowledge could serve as a basis for the implementation of strategies to identify individuals at high risk for vitD deficiency and better target those in need for supplementation. However, we acknowledge that the developed algorithms have some limitations: the moderate sample size and the fact that the study population consisted only of adults living in the same geographical area (city of Sousse-Tunisia) so we cannot ensure that our sample was representative of the population as it was based on subjects from health care personnel at the hospital who may be different from general population.\u003c/p\u003e \u003cp\u003eTherefore, it is imperative to validate the predictive efficacy of the existing algorithms across varied experimental settings and on a more extensive, representative sample of participants. Consequently, a broader study will be conducted across diverse latitudes and cities in Tunisia. Additionally, the incorporation of artificial intelligence and machine learning tools will be pursued to further enhance analysis and insights.\u003c/p\u003e \u003cp\u003eWhile a blood test may remain the best way to measure serum 25(OH)D concentration, having the results regarding vitD status from a simple, reliable screening tool may negate the need for a blood test, and indeed could provide clinicians with the possibility to stratify individuals at heightened risk for vitD deficiency and enable them make well informed decisions regarding the necessity for supplementation to manage this deficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Tunisian Ministry of Higher Education, Scientific Research and Technology and the Ministry of Health for their support. The authors are thankful to all the members of the biochemistry laboratory of Sahloul University Hospital for their cooperation in conducting this research specially Mrs. Henda Falfoul and Mr. Mahmoud Smida. Finally, we gratefully acknowledge the contribution of participating individuals whose cooperation made this study possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAuthor Contribution Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supervised by Pr. Asma Omezzine and Pr. Ali Bouslama. Mariem Ammar, designed the study and was responsible for screening potentially eligible studies. Mariem Ammar, Syrine Heni and Sahbi Tira recruited the patients under the supervision of Dr. Sonia Ksibi. Mariem Ammar and Amani abderrahmane performed the genotyping. Pr. Asma Omezzine and Mariem Ammar performed the statistical analysis and the interpretation of the data. Mariem Ammar wrote the manuscript. Amira Moussa, Yassine Khalij and Haithem Hamdouni contributed in writing the manuscript. Pr. Asma Omezzine and Pr. Ali Bouslama provided a critical review of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData Availability Statement:\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available from the biochemistry department LR12SP11 research laboratory at Sahloul University Hospital in Sousse, Tunisia, upon reasonable request. Access to these data is subject to restrictions under the licensing agreement governing their use in this study. Requests for data should be addressed to the corresponding author, Mariem Ammar.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eCompeting Interests Statement\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by grants from the Tunisian Ministry of Higher Education, Scientific Research and Technology and the Ministry of Health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHolick MF, Binkley NC, Bischoff-Ferrari HA, Gordon CM, Hanley DA, Heaney RP, et al. Evaluation, treatment, and prevention of vitamin D deficiency: an Endocrine Society clinical practice guideline. J Clin Endocrinol metabolism. 2011;96(7):1911\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAspray TJ, Bowring C, Fraser W, Gittoes N, Javaid MK, Macdonald H, et al. National Osteoporosis Society vitamin D guideline summary. Age Ageing. 2014;43(5):592\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolick MF. The vitamin D deficiency pandemic: Approaches for diagnosis, treatment and prevention. Reviews Endocr Metabolic Disorders. 2017;18(2):153\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Oliveira CL, Cureau FV, dos Santos Cople-Rodrigues C, Giannini DT, Bloch KV, Kuschnir MCC, et al. Prevalence and factors associated with hypovitaminosis D in adolescents from a sunny country: Findings from the ERICA survey. J Steroid Biochem Mol Biol. 2020;199:105609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Schoor N, Lips P. Worldwide vitamin D status. 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J Arthroplast. 2016;31(9):1921\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"vitamin D deficiency, polymorphisms, prediction algorithm","lastPublishedDoi":"10.21203/rs.3.rs-4448996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4448996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eAn alarming increase in vitamin D (vitD) deficiency even in sunny regions highlights the need for a better understanding of the mechanisms controlling vitD variability. We aimed to study potential variables involved in vitD deficiency among healthy Tunisian adults in order to establish two prediction algorithms: a composite algorithm (CA) that included genetic and non genetic factors and a simple one (SA) including only environmental non genetic factors. These algorithms could be used to predict vitD status and help identify individuals at high risk of vitD deficiency.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe screened six key genes (DBP, CYP2R1, CYP27B14, CYP24A1 and VDR) within the vitD metabolic pathway using 15 single nucleotide polymorphism (SNP) markers in across a cohort of 394 unrelated healthy individuals. After giving an informed consent, all participants were asked to complete a generalized questionnaire. Significant confounding factors that may influence the variability in serum 25(OH)D levels were used as covariates for association analyses. Statistical study was carried out with SPSS26.0.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eVitD deficiency correlated positively with albumin (r\u0026thinsp;=\u0026thinsp;0.135, p\u0026thinsp;=\u0026thinsp;0.007) and negatively with serum PTH (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.303, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.198, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and BMI (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.04). Multivariate logistic regression revealed that season, sun screen use, phototype, age, VDR- rs2228570 and CYP24A1- rs6013897 were significant predictors of hypovitaminosis D. Non genetic factors explained 15.6% of the variance in 25(OH)D concentrations while genetic polymorphisms (VDR- rs2228570 and CYP24A1- rs6013897) explained a lower variance of 12%. When combined together, genetic and non genetic factors contributed up to 27.6% in 25(OH)D concentrations variability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003e25(OH)D deficiency is highly prevalent among healthy adults in Tunisia. It is related to seasonal fluctuations, increasing age, darker skin tones, excessive sunscreen usage, and genetic polymorphisms in the VDR and CYP24A1 genes. The genetic markers could be used as tools in Mendelian randomization analyses of vitD, and they should well be considered when establishing a supplementation protocol in order to prevent 25(OH)D deficiency in the Tunisian population.\u003c/p\u003e","manuscriptTitle":"Prediction algorithms using genetic and non genetic factors inducing vitamin D deficiency among healthy adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-03 17:13:16","doi":"10.21203/rs.3.rs-4448996/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-05-22T03:56:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-21T10:56:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2024-05-20T12:04:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca88711b-0e35-49e6-8c73-d02bff435269","owner":[],"postedDate":"June 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-06-03T17:13:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-03 17:13:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4448996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4448996","identity":"rs-4448996","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}