A Comprehensive Assessment in Saudi Arabia
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Vitamin D Deficiency: A Comprehensive Assessment in Saudi Arabia
Subject Area: Education / Adult Learning / Biostatistics
Modified: 21st August 2025
Vitamin D deficiency is a major health concern worldwide. Unfortunately, this situation is high is in countries like Saudi Arabia, where exposure to sunlight prevails, however with changing lifestyles, lack of outdoor exposure and dietary factors contributes to vitamin D deficiency as well. Vitamin D is known to play a key role in bone health as well as regulation of calcium-phosphate levels and deficiency leads to a multitude of health problems including it is related to increased risk of osteoporosis, cardiovascular disease, and immune dysfunction. This research will examine vitamin D deficiency prevalence in Saudi Arabia. And also to look at the relationship of demographic factors, age, sex, lab measures including alkaline phosphatate, parathormone and phosphate.
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Table Of Content
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S. No
Contents
Page
1
Demographic characteristics of patients
2
2
Area under the curve
3
3
Coordinates of the curve
6
4
Intraclass correlation coefficients (ICC) for CLIA, RIA and HPLC
7
5
Sample coefficient of variation (SCV) values with 95% confidence intervals
7
6
Linear regression analysis
7
7
Two- way ANOVA
7
8
Correlation between Parathormone and alkaline phosphates
7
Demographic characteristics of patients
| Variables | Total (n=200) | p-value | |
| Male | Female | ||
| Sex | 50 (25) | 150(75) | – |
| Age (years) | 48.1±17.0 (14-76) | 44.8±15.7 (3-80) | 0.209 |
| Chemiluminescent immunoassays | 9.2±0.7 (8.3-13.1) | 9.6±6.3 (7.9-86.0) | 0.610 |
| PO4 | 3.7±0.6 (2.6-4.9) | 4.0±2.9 (1.8-39.0) | 0.506 |
| Alkaline Phosphate | 101.6±47.8 (55-302) | 84.9±30.4 (8.0-208.0) | 0.005 |
| Parathromone (pmol/L) | 7.9±3.5 (3.2-19.8) | 6.6±4.2 (1.4-43.9) | 0.039 |
| 25OHD (nmol/L) | 13.3±7.2 (2.0-34.6) | 14.0±14.0 (3.3-150.0) | 0.723 |
| Radioimmunoassay | 8.3±1.6 (3.0-12.0) | 19.4±13.3 (10.0-150.0) | 0.000 |
| HPLC-D3 | 12.4±4.0 (6.0-25.0) | 24.7±14.5 (12.0-150.0) | 0.000 |
Values are presented as Mean ± SD or n (%); Range in Parenthesis
Out of 200 respondents, 75% were female (150) and 25% male (50). The mean ages were 44.8 years for males and 48.1 years for females. The mean CLIA values for males were 9.2, and for females, 9.6. PO4 levels averaged 3.7 in males and 4.0 in females. Alkaline phosphatase (ALKA PHOS) was 101.6 in males and 84.9 in females. The mean parathormone values were 7.9 for males and 6.6 for females. 25OHD by serial calcitriol was 13.3 in males and 14.0 in females. RIA values for 25OHD were 8.3 for males and 19.4 for females. High-pressure liquid chromatography-D3 (HPLC-D3) showed averages of 12.4 for males and 24.7 for females.
The formula for calculating Confidence interval is
C.I = Mean ±1.96 SD
The Average mean of CLIA-RIA is taken in X-axis and difference of CLIA-RIA is in Y-axis. The mean for CLIA-RIA was 20.2 with maximum of 57.0 and minimum of -16.7. We must mean between the maximum and minimum.
Among the 60 patients suspected of having meningitis, most were female (71.7%) and non-Saudis (73.3%). The most common symptoms at admission were fever (80%), altered mental status (70%), headache (48.3%), neck stiffness (41.7%), seizures (31.7%), vomiting (20%), photophobia (10%), and dizziness (5%) (Figure 2).
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Area under the curve
Test Result Variable(s): Chemiluminescent immunoassays | ||||
Std. Error | Asymptotic Sig. | Asymptotic 95% Confidence Interval |
| |
Lower Bound | Upper Bound |
| ||
.000 | .085 | 1.000 | 1.000 |
|
- Under the nonparametric assumption
- Null hypothesis: true area = 0.5
Importance of ROC Curve
Receiver Operating Characteristic (ROC) curves are used in medicine to determine cutoff values for clinical tests, such as the 4.0 ng/ml cutoff for prostate-specific antigen (PSA) in prostate cancer diagnosis. ROC curves help evaluate test accuracy by plotting sensitivity (true positive rate) against 1-specificity (false positive rate). An ideal test has an area under the curve (AUC) of 1.0, indicating 100% accuracy, while a random test has an AUC of 0.5. The ROC curve will help us to determine optimal cutoff values that minimize false positives and false negatives. Sensitivity, specificity, and positive predictive value (precision) are key metrics derived from the curve. Comparing ROC areas between tests can determine the most accurate method.
Coordinates of the curve
| Coordinates of the Curve | ||
| Test Result Variable(s): Chemiluminescent immunoassays | ||
| Positive if Greater Than or Equal To | Sensitivity | 1 – Specificity |
| 6.900 | 1.000 | 1.000 |
| 8.000 | 1.000 | .995 |
| 8.150 | 1.000 | .985 |
| 8.250 | 1.000 | .975 |
| 8.350 | 1.000 | .955 |
| 8.450 | 1.000 | .940 |
| 8.540 | 1.000 | .899 |
| 8.590 | 1.000 | .894 |
| 8.650 | 1.000 | .844 |
| 8.750 | 1.000 | .769 |
| 8.850 | 1.000 | .714 |
| 8.950 | 1.000 | .628 |
| 9.050 | 1.000 | .543 |
| 9.150 | 1.000 | .442 |
| 9.250 | 1.000 | .347 |
| 9.350 | 1.000 | .266 |
| 9.450 | 1.000 | .221 |
| 9.550 | 1.000 | .141 |
| 9.650 | 1.000 | .101 |
| 9.750 | 1.000 | .070 |
| 9.850 | 1.000 | .050 |
| 9.950 | 1.000 | .035 |
| 10.050 | 1.000 | .030 |
| 10.150 | 1.000 | .025 |
| 10.300 | 1.000 | .015 |
| 11.450 | 1.000 | .010 |
| 12.800 | 1.000 | .005 |
| 49.550 | 1.000 | .000 |
| 87.000 | .000 | .000 |
a. The lowest observed cutoff value is the lowest observed test value – 1. The highest cutoff value is the maximum recorded test value increased by 1. All the other cutoff values were averages and are averaged of two consecutive ordered observed test values.
Intraclass correlation coefficients (ICC) for CLIA, RIA and HPLC
| Intraclass Correlation (ICC) (95% CI) | P-value | |
| Single Measures | 0.448 (0.363-0.531) | 0.000 |
| Average Measures | 0.709 (0.631-0.772) | 0.000 |
| Intraclass Correlation (ICC) (95% CI) | P-value | |
| CLIA-RIA | ||
| Single Measures | 0.033 (-0.106-0.171) | 0.319 |
| Average Measures | 0.065 (-0.236-0.292) | 0.319 |
| CLIA-HPLC | ||
| Single Measures | 0.022 (-0.117-0.160) | 0.379 |
| Average Measures | 0.043 (-0.265-0.276) | 0.379 |
| RIA-HPLC | ||
| Single Measures | 0.941 (0.923-0.955) | 0.000 |
| Average Measures | 0.970 (0.960-0.977) | 0.000 |
To estimate the accuracy in each method the Intra-class correlation coefficients (ICC) for HPLC, RIA and CLIA was carried out with 95% of confidence interval. The RIA-HPLC had substantially better ICC compared to CLIA-RIA and CLIA-HPLC, and CLIA-RIA had better ICC than, CLIA-HPLC.
Sample coefficient of variation (SCV) values with 95% confidence intervals
Variable | Mean | SD | SCV (%) (95% CI) |
Chemiluminescent immunoassays (CLIA) | 9.5 | 5.5 | 57.9 (8.7-10.3) |
Radioimmunoassay (RIA) | 16.6 | 12.5 | 75.3 (14.9-18.4) |
High-pressure liquid chromatography-D3 (HPLC-D3) | 21.7 | 13.8 | 63.6 (19.7-23.6) |
The precision was determined by the Sample coefficient of variation (SCV). RIA has a SCV of 75.3% mean of 9.5 and S.D 5.5, HPLC-D3 had a mean of 21.7, SD of 13.8 and SCV of 63.6% and CLIA has a average of 9.5, S.D of 5.5 and SCV of 57.9%.
Linear regression analysis of the relationship between the absolute differences of CLIA, RIA, and HPLC and their respective means
| Unstandardized Coefficients | t | P-value | 95.0% Confidence Interval | |||
| B | Std. Error | Lower Bound | Upper Bound | |||
| (Constant) | 10.036 | 1.492 | 6.728 | .000 | 7.095 | 12.978 |
| Mean – CLIA-RIA | -1.312 | .101 | -12.994 | .000 | -1.511 | -1.113 |
| (Constant) | 10.080 | 1.645 | 6.126 | .000 | 6.835 | 13.325 |
| Mean – CLIA-HPLC | -1.426 | .095 | -14.984 | .000 | -1.614 | -1.239 |
| (Constant) | -3.069 | .547 | -5.614 | .000 | -4.148 | -1.991 |
| Mean – RIA-HPLC | -.103 | .024 | -4.365 | .000 | -.150 | -.057 |
Dependent variable: Difference between CLIA, RIA and HPLC
The non uniformity variability is found using linear regression analysis, in this table it is clear that beta coefficient of Mean – CLIA-RIA where the values are beta=-1.312, t=-12.994, are lesser than p<0.05 and the significance is less than alpha of 0.05 value the null hypothesis is rejected. Thus, there is significant association between HPLC and Difference of CLIA, RIA. The beta coefficient Mean CLIA- HPLC values are beta=-1.426, t=-14.984 are lesser than p<0.05 and the significance is less than alpha of 0.05 value the null hypothesis is rejected. Thus there is significant association between RIA and difference of CLIA and HPLC.
The non uniformity variability is found using linear regression analysis, in this table it is clear that beta coefficient of Mean – CLIA-RIA where the values are beta=-1.312, t=-12.994, are lesser than p<0.05 and the significance is less than alpha of 0.05 value the null hypothesis is rejected. Thus, there is significant association between HPLC and Difference of CLIA, RIA. The beta coefficient Mean CLIA- HPLC values are beta=-1.426, t=-14.984 are lesser than p<0.05 and the significance is less than alpha of 0.05 value the null hypothesis is rejected. Thus there is significant association between RIA and difference of CLIA and HPLC.
Two- way ANOVA
| Tests of Between-Subjects Effects | ||||||
| Dependent Variable: PARATHROMONE | ||||||
| Source | Type III Sum of Squares | df | Mean Square | F | Sig. | Partial Eta Squared |
| Corrected Model | 3175.700 | 189 | 16.803 | 2.997 | .108 | .991 |
| Intercept | 6694.348 | 1 | 6694.348 | 1194.014 | .000 | .996 |
| AGE | 363.371 | 56 | 6.489 | 1.157 | .489 | .928 |
| SEX | 2.402 | 1 | 2.402 | .429 | .542 | .079 |
| ALKPHOS | 1213.483 | 72 | 16.854 | 3.006 | .108 | .977 |
| AGE * ALKPHOS | 155.494 | 21 | 7.404 | 1.321 | .410 | .847 |
| Error | 28.033 | 5 | 5.607 | |||
| Total | 12572.747 | 195 | ||||
| Corrected Total | 3203.733 | 194 | ||||
| a. R Squared = .991 (Adjusted R Squared = .660) | ||||||
Since p value is greater than 0.05 for Age (F= 1.157, p=0.489), sex (F=0.429, p= 0.542), ALKPHOS (F=3.006, p=0.108), and for interaction effect of age and ALKPHOS (F=1.321, p= 0.140), the null hypothesis is accepted at 5% level of significance, Hence it is concluded that there is no significant difference between PARATHROMONE with respect to the Age ,Sex, ALKPHOS and Interaction effect of age and ALKPHOS.
Correlation between Parathormone and alkaline phosphates
| PARATHROMONE | ALKA PHOS | ||
| PARATHROMONE | r-value | 1 | .129 |
| p-value | .072 |
The Pearson correlation analysis shows the linearity between the variables, and the table depicts a positive linear relationship with Alka Phos (r = 0.129, p >0.001) and thus the p values is greater than 0.05 it is stated that there is no relationship between Parathromone and Alka Phos.
Conclusion
Results of this study demonstrate that vitamin D deficiency is at an alarming level in Saudi Arabia, more so among females as evidenced by laboratory results showing differential biomarkers of alkaline phosphatase, parathormone, and phosphate. The correlational results highlight the distinctly different measurement methods (CLIA, RIA, and HPLC) of vitamin D and suggest that some had better accuracy than others. The study failed to demonstrate an significant association between vitamin D status and age, sex, and alkaline phosphatase levels. Overall, our results suggest public health initiatives are needed regarding vitamin D deficiency and improving dietary considerations and sun exposure. A recommendation for future research regarding accurate diagnostic assessments of vitamin D is made.