A Review of Anthropometric Measurements for Saudi Adults and Elderly, Directions for Future Work and Recommendations to Establish Saudi Guidelines in Line with the Saudi 2030 Vision
Abstract
:1. Introduction
2. Materials and Methods
3. Measures and Anthropometrics of Saudis
3.1. Facial Anthropometrics
3.2. Saudi Stature, Predictions from Hand and Foot Dimensions and Comparison to Other Populations
3.3. Other Anthropometrics Assessed in Healthy Saudis
4. Results: Studies Conducted in Saudi Arabia Providing a National Reference of Anthropometric Measurements and Comparing Them with International Standards
No | Author, Year (Reference) | Study Design | Region/City | Population Age | Sample n | Anthropometrics Assessed | Anthropometrics Assessment Definition | Comments | Established Cutoffs for Saudis |
---|---|---|---|---|---|---|---|---|---|
Adults | |||||||||
1. | Almajwal et al., 2009 [63] | Cross-sectional | Eastern | ≥30 years | 197,681 | Weight, height, BMI | WHO | Anthropometric measurement method mentioned. The study assessed the ability of BMI to diagnose obesity and to determine the optimal BMI cutoff points for the Saudi population based on the prevalence of diabetes and hypertension. | There is an increased risk of diabetes and hypertension relative to BMI, starting at a BMI as low as 21 kg/m2, but overall, there is no BMI cutoff with high predictive value for the development of these chronic diseases, including the WHO definition of obesity at BMI of 30 kg/m2. |
2. | Albassam 2016 [64] | Cross-sectional | Riyadh | 18–70 years | 700 | Weight, height, BMI, waist and neck circumference, % body fat | Based on references | Anthropometric measurement method mentioned. | The appropriate neck circumference to predict three or more metabolic risk factors in Saudi women is 35.5 cm. |
3. | Al-Rubean et al., 2017 [17] | Cross-sectional | All regions | ≥18 years | 12,126 | Weight, height, BMI, waist circumference | WHO/IDF | Anthropometric measurement method mentioned. The study identified the optimal cutoff values for anthropometrics for identifying the risk of metabolic syndrome. | The optimal cutoff values for identifying the risk of metabolic syndrome:
|
4. | Al-Kahtani 2017 [59] | Cross-sectional | Riyadh | 20–35 years | 232 | Weight, height, waist circumference, body composition | Not mentioned | Anthropometric measurement method mentioned. The study identified the mean cutoff values for sarcopenia indices in Saudi men. |
|
5. | Alfadhli et al., 2017 [56] | Cross-sectional | Madinah | ≥18 years | 785 | Weight, height, waist and neck circumference | NCEP ATP III | Anthropometric measurement method mentioned. The study determined the optimal cutoff value for neck circumference to identify. overweight/obesity and predict cardiometabolic risk. | Neck circumference cutoffs for identifying participants with central obesity
|
6. | Alkhalaf 2017, [54] | Cross-sectional | Saudi national surveys | Adults | 23,968 | Weight, height, BMI, waist circumference, waist-to-hip ratio waist to height ratio, body composition | WHO and Several references | Ph.D. thesis (chapters 5–7). Anthropometric measurement method mentioned. Author provided several tables of diagnostic performance of anthropometrics in predicting health morbidities in Arab adults. The study suggested new cutoff values for BMI, waist circumference, waist-to-hip/height ratio for Saudi adults. | WC cutoffs for Saudis
|
7. | Alzeidan et al., 2019 [57] | Cross-sectional | Riyadh | 18–85 years | 3063 | Weight, height, BMI, neck, hip, and waist circumference | Based on several references | Anthropometric measurement method mentioned. The study showed that neck circumference was a predictor of obesity and metabolic syndrome. The study provided neck circumference cutoffs that predict obesity. | NC cutoffs to predict obesity
|
8. | Al-Hanawi et al., 2020 [65] | Cross-sectional | All regions | >15 years | 7746 | Weight, height, BMI | Natural log of BMI | Anthropometric measurement method referred to a reference. Representative sample from the Saudi Health Interview survey. The study decomposed the BMI gender gap into its associated factors across the entire BMI distribution by using counterfactual regression. methods. Females showed a higher BMI than males. | The study used new distribution-based regression methods to explain the BMI gender gap. The advantage of this method is that the study observed heterogeneity in how determinants are associated with BMI differentials at various points of distribution. |
9. | Almousa 2021 [66] | Cross-sectional | 4 regions | 18–63 | 1074 | Weight, height, BMI, and other anthropometrics | ISO/ASTM standard | 3D body scanner | In this study, the first anthropometric database for the Saudi female population was established using 3D body scanning technology, and a sizing system for this target population was developed. |
10. | Shaheen et al., 2021 [61] | Cross-sectional | Riyadh | 19–25 years | 139 | Weight, height, BMI | Not mentioned | Anthropometric measurement method mentioned. | This study established the hand grip strength and pinch strengths normative values and developed the prediction equations in a sample of healthy female college students. |
Elderly | |||||||||
11. | Alqahtani et al., 2019 [62] | Cross-sectional | Riyadh | 65–80 years | 1048 | Weight, height, BMI, arm circumference | Not mentioned | Anthropometric measurement method mentioned. | This study is the first that established normative values of hand grip for older adults in Saudi Arabia. |
12. | Bindawas et al., 2019 [67] | Cross-sectional | Riyadh | ≥60 years | 2045 | Weight, height, BMI, handgrip strength | Compared hand grip strength to other populations | The study established normative data for handgrip strength in older Saudi adults. |
5. Discussion
5.1. Recommendations for Establishing Saudi Guidelines of Anthropometric Measurements and Directions for Future Work in Line with the Saudi 2030 Vision
5.2. General Recommendations and Directions for Future Work
Recommendations- Adults/Elderly | Directions for Future Work |
---|---|
1. Cutoffs of BMI, waist circumference and metabolic syndrome for Saudi adults >19 years from a representative sample [17,54] are available (see Section 4) (Table 1) |
|
2. Create standard cutoffs of BMI, waist circumference, skinfold thickness, muscle and fat mass and hand grip strength for Saudi elderly |
|
3. Create a standard guideline on how to prevent malnutrition, loss of muscle mass, and increase protein intake of Saudi elderly in Arabic |
|
Recommendations for individuals with health conditions and disabilities | Directions for future work |
4. Create screening programs for health conditions that are related to anthropometrics similar to Singapore [86], obesity, hypertension, diabetes mellitus, and hyperlipidemia | Establish a database of the population’s anthropometrics and explore their relation to obesity, hypertension, diabetes mellitus, and hyperlipidemia to facilitate identifying trends over the years, causes, and implications for policy makers and preventive measures needed |
General recommendations and directions for future work | |
5. Link medical health records from all medical facilities in Saudi Arabia with the Saudi national ID or resident ID number for non-Saudis to establish national data. 6. After linking medical health records with ID number, create a unified national health record system used by all health institutions in Saudi Arabia [10,87] and report to the MOH similar to the NHS used in the UK [88]. This national health record system will include anthropometrics from birth, diagnoses, lab tests, medications, and surgeries. The national health record system may decrease the repetition of medical examinations and lab tests. In addition, it assists healthcare providers in formulating trends of disease and anthropometrics for patients. The data from the national health record system will help in identifying the prevalence, trends, the causes of diseases and conducting preventive measures. 7. Diet is a modifiable factor that affects anthropometric measurements and body composition [74]. Collecting the diet of Saudis from nationally representative samples [75] and creating a national diet and nutrition survey (NDNS) similar to the one in the UK [76] is essential. The NDNS may collect physical activity data [78] that is accessible to conduct statistical analyses and explore the relationship between different food items, dietary habits and physical activity with anthropometric measurements, body composition, and NCDs. 8. Study the associations between genes and anthropometric traits and their association with cardiometabolic outcomes and other diseases in a representative sample of the Saudi population as conducted in two meta-analyses [81,82]. These associations are in line with the Saudi Genome Program in the 2030 Saudi vision [9]. |
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Noorwali, E.A.; Aljaadi, A.M. A Review of Anthropometric Measurements for Saudi Adults and Elderly, Directions for Future Work and Recommendations to Establish Saudi Guidelines in Line with the Saudi 2030 Vision. Healthcare 2023, 11, 1982. https://doi.org/10.3390/healthcare11141982
Noorwali EA, Aljaadi AM. A Review of Anthropometric Measurements for Saudi Adults and Elderly, Directions for Future Work and Recommendations to Establish Saudi Guidelines in Line with the Saudi 2030 Vision. Healthcare. 2023; 11(14):1982. https://doi.org/10.3390/healthcare11141982
Chicago/Turabian StyleNoorwali, Essra A., and Abeer M. Aljaadi. 2023. "A Review of Anthropometric Measurements for Saudi Adults and Elderly, Directions for Future Work and Recommendations to Establish Saudi Guidelines in Line with the Saudi 2030 Vision" Healthcare 11, no. 14: 1982. https://doi.org/10.3390/healthcare11141982
APA StyleNoorwali, E. A., & Aljaadi, A. M. (2023). A Review of Anthropometric Measurements for Saudi Adults and Elderly, Directions for Future Work and Recommendations to Establish Saudi Guidelines in Line with the Saudi 2030 Vision. Healthcare, 11(14), 1982. https://doi.org/10.3390/healthcare11141982