Anthropometric Measurements and the Limitations of Assessment of Nutritional Status

Introduction

The use of anthropometric measurements in nutritional assessment entails the interpretation of physical measurement of an individual in comparison to standardised population reference values (Jelliffe, 1966). Anthropometric indicators can be used in assessing nutritional status at an individual level or in larger epidemiological studies (Sellen, 1998). As well as being applicable to different population sizes, anthropometric measurements are inexpensive to generate and are generally non-invasive techniques (Sellen, 1998).  Anthropometry is also has uses in clinical circumstances, gaining information on nutritional screening, in nutritional surveillance and in monitoring (Tomkins, 1994). Generating anthropometric data can be limited by random and systematic measurement errors. Error in these measurements should be estimated, assessed and minimised. Imprecision can occur in both inter-observer and intra-observer measurement error. Intra-observer measurement error is of more relevance during large epidemiological studies where many anthropometrists are participating. A way to minimise this bias may to be to ensure that only very simple anthropometric measurements are taken such as weight and height. Undependability summarises the factors that may affect the replication of the measurements such as unpredictable variances in measurements (Ulijaszek & Kerr, 1999). Systematic errors such as uncalibrated instruments can add to the inaccuracies of measurements.  Anthropometric measurements and indices are important indicators of nutritional status giving important insights into conditions such as malnutrition, over- or underweight  and obesity (Castillo- et al. 2012).  Anthropometric measurements, although practical and simple to measure,  can be considered less accurate in isolation, than clinical or biochemical measurements in assessing nutritional status (Gorstein et al. 1988). Specifically, assessing clinical signs may indicate a nutritional deficiency. A combination of dietary intake data, clinical assessment, anthropometric measurements and biochemical markers may give a more comprehensive overview of nutritional status for the individual (Burns et al. 1988), however anthropometry has advantages in assessing nutritional status in large studies.

Objectives

Critically compare a dataset of anthropometric measurements from a population of 180 individuals for nutritional status.

Methodology

Anthropometric measurements

Anthropometric measurements were taken by trained anthropometrists from a group of 180 anonymous volunteers and the data set was provided. Only calibrated equipment for measuring was used. Age was self-reported by the subjects. All measurements were taken 3 times with mean recorded.

Height (cm)

In bare feet, subjects stood on a flat surface with weight evenly distributed over both feet. Heels faced forward, and head, buttocks and heels touched the vertical wall. The measuring board on the standometer was brought to rest on the subject’s head.

Weight (kg)

The subject was weighed with feet facing forward on the scales, weight evenly distributed.

Umbilical waist (cm), Natural waist, hip (cm) and mid-upper-arm circumference (cm)

For waist and hip measurements, subjects stood with arms folded tightly across the chest. The tape was placed in a horizontal plane and tightened while not causing indentation. Natural waist was taken at the narrowest point between the lower costal rib and the top of the iliac crest. Umbilical waist was taken at the level of the umbilicus. Hips were measured at the widest point of the buttocks with feet together. For mid-upper-arm circumference (MUAC) measurement was taken at the midpoint at the midway between the shoulder tip and the elbow tip on the left arm with arms hanging freely.

BMI

Body Mass Index (BMI) (kg/m²) was calculated using the formula:

BMI = Weight(kg) / Height (m²) (Cdc.gov, 2018)

WHR

Waist-hip Ratio (WHR) was calculated using the formula:

WHR = Waist Circumference (cm) / Hip Circumference (World Health Organization, 2018)

Results

Anthropometric measurements were taken from a group of 180 subjects which was comprised of 163 females and 17 males. The age of participants ranged from 18-48 (years) however the mean age was just 20 with a standard deviation of 3. Population data is summarised in Table 1 where the results are presented as averages, standard deviation and range. Table 2 compares male and female participants anthropometric measures and indices. Comparison between genders expressed as mean and standard deviation showed differences in all measurement averages and indices between the genders except for waist-height ratios. Figure 1 shows both gender populations expressed as percentages, body mass indices classified as underweight, normal, overweight, obese according to Cdc.gov, 2018. A greater percentage of females were classified as having a normal BMI of between 18.5 and 25 compared to males. Males have a greater percentage (47%) classified as overweight or obese compared to the female population. Figure 2 compares the natural waist circumference of males and female populations, expressed as percentages and using classifications provided by the World Health Organisation, 2018. This shows that 29% of females have a waist of greater than 80cm giving an increased, or substantially increased risk of developing metabolic complications compared to only 12% of males measured. When the mean waist-hip ratio for each gender is considered in Table 2, both are below the WHO cut-offs for this at greater than or equal to 0.90 cm for males and  greater than or equal 0.85 cm for females for substantially increased risk of metabolic complications. The mean  of the natural waist: hip ratio for females at 0.76 is much lower than the umbilical waist -hip ratio at 0.81 compared to the cut-off of 0.85. Whereas the gender mean for males for umbilical and natural waist are very similar at 0.86 and 0.84 respectively, compared to the cut-off of 0.90.

Discussion

Increased BMI or body weight has been correlated with an increased risk of developing metabolic and cardiovascular disease (Go et al.2013). Of the participants studied, 47% of the male group had an BMI that would be considered overweight or obese with only 34% of the female group. In considering risk to health in relation to overweight or obesity, accuracy of this diagnoses is essential, and BMI is limited in not considering lean-mass distribution (Adab et al. 2018). As there was access to several more anthropometric measurements with this population, more of a comprehensive overview of nutritional status was possible. Both umbilical, natural waist circumference and hip measurements were taken allowing waist: hip ratios to be considered. This allowed consideration of body fat distribution alongside body weight. Waist circumference gives an indication of abdominal fat which has been considered a risk factor for both cardiovascular and metabolic disease (Klein et al. 2007).  A greater percentage of the female population 29% had a waist circumference indicating an increased risk to health compared to the male population at only 12%. This would indicate that although body weight in relation to height may be considered normal for most of the female population, prevalence of increased abdominal fat could indicate a greater risk to health. The mean of MUAC in table 2 indicates more males approach the 50^th percentile than females. A higher MUAC has been correlated to a greater risk to health in overweight and obesity (Benítez et al. 2016). However, an inverse relationship has been observed in healthy, normal weight individuals (Wu et al. 2017) and this should also be considered when assessing the anthropometric indices for this group of males. It should be considered that both the sample size and age range were limitations to this data-set with only 17 males participating and the age range for both genders being restricted to mainly participants in their early 20s. Since only 17, relatively young males participated, it is unlikely to be reflective of the wider male population. With further study an improvement would be to increase the sample size and to include dietary intake data, biochemical marker analysis and clinical data alongside the anthropometric measurements.

Conclusions

Anthropometric measurements and indices can be considered as simple and non-invasive techniques to measure nutritional status in a population. Accuracy of nutritional assessment could be improved with increasing sample size and including dietary intake, biochemical marker and clinical data.

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Appendix 1 – Tables and Figures

Figure 1: Percentage of Males and Females classified as underweight, normal, overweight, obese according to Cdc.gov. (2018)

Figure 2: Natural waist circumference (cm) classified by risk of metabolic complications and gender (WHO, 2018)