Stature estimation forms a key element in reconstructing a biological profile during forensic and medico-legal investigations, particularly in cases involving mass disasters, homicide, or accidents where dismembered or skeletal remains are recovered [1,2]. Anthropometry, the scientific study of human body measurements, has established itself as a cornerstone in biological anthropology and remains indispensable in forensic science, where it is widely used for personal identification and analysis of human remains in both medico-legal and clinical contexts [14]. Stature, a crucial identity marker, can be estimated using various body parts via anthropometric methods. Regression analysis, which predicts stature from bone lengths, is particularly useful when only isolated body parts are available [3,4].

The proportional relationships between body segments and stature vary among populations due to genetic, nutritional, environmental, and geographic factors [5]. Therefore, stature estimation formulas must be population-specific to ensure precision [7]. Several Indian studies have addressed this need across regions such as Kerala, Maharashtra, and parts of Gujarat, confirming regional variation in regression constants.

While studies exist on individual upper limb bones in Gujarat, comprehensive data encompassing the humerus, radius, and ulna for the North Gujarat population are limited. This study aims to fill this gap by establishing reliable regression equations for stature estimation using percutaneous measurements of these three major upper limb bones.

MATERIALS AND METHODS

Study Design: A cross-sectional observational study was carried out in North Gujarat.

Sample and Sampling

Three hundred healthy adults (150 males, 150 females) aged 20–50 years were selected through convenience sampling. The sample size aligns with comparable anthropometric studies.

Inclusion Criteria

• Healthy adults aged 20–50 years native to North Gujarat.

Exclusion Criteria

• Individuals with fractures, deformities, congenital anomalies, or skeletal disorders affecting bone growth or stature.

Anthropometric Measurements

All measurements were conducted by a single trained investigator using standard techniques to minimize inter-observer error. Participants were measured in the afternoon to avoid diurnal variation.

1. Stature: Measured using a stadiometer with the subject standing barefoot in the Frankfurt plane; recorded to the nearest 0.1 cm.
2. Humerus Length: Measured from the acromion process to the lateral epicondyle using a spreading caliper with the elbow flexed at 90°.
3. Radius Length: Measured from the radial head to the tip of the styloid process using a sliding caliper with the forearm in mid-pronation.
4. Ulna Length: Measured from the olecranon process to the styloid process with the forearm fully pronated.

Each measurement was taken thrice, and the mean was used for analysis.

Statistical Analysis

Data were analyzed using SPSS version 26. Descriptive statistics (mean ± SD) were computed for all variables. Pearson’s correlation determined the relationship between stature and bone lengths. Simple and multiple linear regression models were developed for both sexes and the combined sample. The R² value and SEE assessed predictive accuracy. A p-value < 0.05 was considered statistically significant.

RESULTS

Descriptive Statistics and Correlation

The mean age of participants was 32.8 ± 8.0 years. Males had significantly greater mean values for stature and bone lengths compared to females (p < 0.001) (Table 1).

All upper limb bone lengths correlated positively and significantly with stature (p < 0.001), with the ulna showing the strongest relationship in both sexes (Table 2).

Table 1: Descriptive Statistics (Mean ± SD)

Table 2: Pearson’s Correlation Coefficient (r) Between Stature and Bone Lengths

All correlations significant at p < 0.001.

Regression Equations for Stature Estimation

1. Sex-Combined (n = 300)
2. Ulna: Stature = 60.45 + 2.54 × Ulna (R² = 0.75; SEE = 4.3 cm)
3. Humerus: Stature = 41.15 + 3.90 × Humerus (R² = 0.65; SEE = 4.3 cm)
4. Radius: Stature = 71.32 + 1.97 × Radius (R² = 0.55; SEE = 5.4 cm)
5. Humerus + Ulna: Stature = 30.12 + 2.12 × Humerus + 1.44 × Ulna (R² = 0.79; SEE = 3.6 cm)
6. Sex-Specific

Males (n=150):

• Ulna: Stature = 65.14 + 2.40 × Ulna (R² = 0.66; SEE = 3.7 cm)
• Humerus: Stature = 45.22 + 3.80 × Humerus (R² = 0.61; SEE = 4.3 cm)
• Humerus + Ulna: Stature = 35.14 + 2.32 × Humerus + 1.27 × Ulna (R² = 0.73; SEE = 3.2 cm)

Females (n=150):

• Ulna: Stature = 54.87 + 2.75 × Ulna (R² = 0.63; SEE = 3.8 cm)
• Humerus: Stature = 38.90 + 4.30 × Humerus (R² = 0.56; SEE = 4.2 cm)
• Humerus + Ulna: Stature = 28.43 + 2.24 × Humerus + 1.55 × Ulna (R² = 0.67; SEE = 3.6 cm)

DISCUSSION

The present findings reaffirm the strong positive correlation between upper limb bone lengths and stature in the North Gujarat population, consistent with prior research across India and abroad. The ulna proved to be the most accurate single bone for stature estimation, corroborating results by Yadav et al. [13] and Thummar et al. [9].

The ulna’s superior reliability may be attributed to its subcutaneous course and easy palpability, which minimizes measurement error. The humerus also demonstrated a high correlation due to its proportional relationship with overall limb length. The radius, though a significant predictor, consistently yielded lower correlations, in line with other studies.

Comparison with earlier studies in Gujarat shows minor variations in regression constants and coefficients, underscoring the influence of regional genetic diversity even within a single state. Multiple regression equations combining humerus and ulna improved accuracy (higher R², lower SEE), aligning with global trends.

The limitation of the study is that percutaneous measurements may differ slightly from direct osteometric data. Future research using skeletal or radiographic measurements is recommended. These equations apply only to adults; separate analyses are needed for adolescent and elderly groups.

Declaration:

Conflicts of interests: The authors declare no conflicts of interest.

Author contribution: All authors have contributed in the manuscript.

Author funding: Nill

CONCLUSION

This study establishes population-specific regression equations for stature estimation based on humerus, radius, and ulna lengths in the North Gujarat adult population. Among these, the ulna is the most dependable single predictor. The formulated equations offer valuable tools for forensic and anthropological identification, especially in cases involving partial upper limb remains. Adoption of such region-specific equations will enhance the precision of stature estimation in forensic practice.

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