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close this bookThe Functional Significance of Low Body Mass Index (IDECG, 1992, 203 p.)
close this folderBody mass index: its relationship to basal metabolic rates and energy requirements
View the document(introduction...)
View the documentIntroduction
View the documentNutritional anthropometric indices and their relationship to BMR
View the documentDo population groups in developing countries in the tropics have lower BMRs?
View the documentLow BMIs, BMRs and energy requirements
View the documentChanges in body weights and stature and their influence on BMI and energy requirements
View the documentReferences
View the documentDiscussion


P. S. Shetty1,2, M. J. Soares1 and W. P. T. James3

1Nutrition Research Centre, St John's Medical College, Bangalore, India; 2Department of Public Health & Policy, London School of Hygiene and Tropical Medicine, London; and 3Rowett Research Institute, Greenburn Road, Bucksburn, Aberdeen, UK

Correspondence to: Professor W. R T. James.


Two significant developments over the last decade have influenced our understanding of energy requirements of humans and their implications in arriving at the numbers of individuals in population groups worldwide who are undernourished and do not receive adequate levels on a daily basis. The first major development has been the recommendation of the FAD/WHO/UNU Expert Consultation on Energy and Protein Requirements (1985) to (a) rely, as a matter of principle, on estimates of energy expenditure (actual or desirable) to arrive at estimates of energy requirements, and (b) to use the basal metabolic rate (BMR) factorial approach for the assessment of total energy expenditure of individuals, communities and population groups. The second advance, more recently made, has been the suggestion that nutritional anthropometric measures, more specifically the use of body mass index (BMI) could be a simple, reliable and easily obtainable objective anthropometric criterion for both the definition and diagnosis as well as an estimate of the severity of undernutrition or chronic energy deficiency (CED) in adults (James et al., 1988).

This paper will discuss the relationships between BMI and BMR and their implications in arriving at estimates of energy requirements of adults both in developed and developing countries. It will also compare the relationships of estimates of BMR derived from other nutritional anthropometric variables such as body weight and height. Evaluation of the lower BMRs of populations in tropical regions - and in particular of those who are likely to be chronically undernourished - and their relationship to BMI will be carried out. Whether all individuals below an acceptable cut-off for BMI (i.e. BMI <18.5) are in fact suffering from CED and the implications for estimating energy requirements will also be explored. Finally, we will consider the effect in the age profile of populations and the impact of secular changes in stature in communities worldwide on the BMR and energy requirements.

Nutritional anthropometric indices and their relationship to BMR

Several anthropometric parameters, such as body weight and height, as well as their transformations, such as BMI, show associations with BMR. The extensive analysis by Schofield, Schofield & James (1985) seemed to indicate that when BMR was plotted against weight (or height) the relationship appeared to be quadratic, cubic or of a more complicated form although there was a strong linear component. Frans (1981) had earlier dealt with a similar problem by computing semi-logarithmic regression equations split into four weight groups thus representing the relationship by a series of straight lines fitted along a full curve. It is now generally accepted that weight provides the best predictor for BMR (Schofield et al., 1985; Soares & Shetty, 1988) since the two are linearly related on regression analysis. However, the addition of terms for height and BMI to such equations does not improve the quality of prediction.

More recent analyses of a reasonably large database on BMRs measured prospectively and carefully collected from well-nourished, adult, male Indians within age ranges 18-30 years suggested that height and BMI contribute roughly in equal measures to variations in BMR (Soares & Shetty, 1988). The correlation coefficients in the series reported by Soares & Shetty (1988) for multiple linear regressions using BMR as the dependent variable were as follows: weight alone, multiple r = 0.80 (r2 = 0.64); weight + age, multiple r = 0.81 (r2 = 0.65); weight + BMI + age, multiple r = 0.81 (r2 = 0.65). The addition of other variables makes hardly any difference to the strong correlations that body weight has with BMR. However, in the 30-60 year age group of adult males the inclusion of age increased the explained variance in BMR by 5.3% (Soares, Francis & Shetty, 1993) which is in contrast to Schofield's observation that age made little difference to the final prediction equation relating BMR to body weight for males >18 years old. A careful examination of the analysis of data by Rand (1982) confirms, for both males and females separately, that single equations could be successfully fitted using a single independent anthropometric variable (or its transformation such as log of weight or square of log of weight) and that the addition of several variables such as age or height added nothing more to the prediction other than a small increase in noise.

Comparison of the correlation coefficients obtained by both Schofield and by Soares & Shetty (1988) shows that BMR has a stronger correlation with body weight than with any other nutritional anthropometric index used as a single independent variable. In Schofield's analysis (1985) the r value for body weight was 0.875 for males (n = 4809) and r = 0.851 for females (n = 2364). The correlations for height alone were r = 0.87 and r = 0.886 for similar numbers of males and females respectively. It is important to point out that the relationships are not perfect and the high correlations obtained with height are largely the result of the contributions of large numbers in the database.

The analysis by Soares & Shetty (1988) demonstrated an r = 0.80 for body weight accounting for 64% of the variance while BMI had an r = 0.64 and height an r = 0.57, thus explaining only 41% and 33% of the variance respectively. This association between BMI and BMR, seen in both well-nourished adults and those with low BMIs in developing countries, has also been reported in well-nourished populations and those with increasing degrees of obesity in the West (Garrow et al., 1988).

BMI is thus not as useful a predictor of BMR as body weight and so is not the most useful index for predicting the BMR of individuals or population groups when applying the factorial method to estimate human energy requirements. However, since BMI has approximately the same value for short, medium height and tall individuals, being independent of stature (Khosla & Lowe, 1967), the index simplifies the approach to estimating the acceptable or optimum body weight for that height among groups of different heights in a population. BMR, and consequently total energy expenditure, can be predicted from the derived optimum body weight, and thus BMI may be a useful addition in arriving at desirable levels of energy requirements of individuals or populations (Shetty & James, 1994).

Do population groups in developing countries in the tropics have lower BMRs?

At the behest of those international agencies (FAO, WHO & UNU) responsible for the Expert Consultation on Energy and Protein Requirements (1985), Schofield et al. (1985) derived predictive equations for BMR of both sexes based on body weight as the most suitable nutritional anthropometric predictor. BMR measurements of Indians constituted one of the largest single ethnic groups from the tropics. During these computations, Schofield made the observation that when the measured BMRs of Indians in this vast database were compared with the predicted value derived from equations obtained after excluding Indians there was a significant lack of fit. The actual, measured BMRs of Indians were 11.2% and 9.9% lower for males and females respectively compared with the data on Europeans and North Americans. A plot of BMR vs body weight and BMR/weight vs body weight revealed that even for the same body weight, Indians on average had lower BMRs. Lack of sufficient data at that stage precluded any extension of this analysis to other Asiatic groups, although an earlier analysis by Quenouille et al. (1957) had suggested the same. A more recent analysis by Henry & Rees (1988) also provided further evidence to support these observations and implied that this phenomenon of a lower BMR in tropical populations was not unique to Indians but was found in other Asiatic groups as well.

Obviously, this observation has enormous implications for the estimates of energy requirements (based on the BMR factorial method) of population groups in developing countries, these being located mostly in the tropical belt. The bulk of the Indian data used by Schofield were reported over 30-40 years ago (Mason & Benedict, 1931; Niyogi, Patwardhan & Modecai, 1939; Sokhey & Malandkar, 1939), but were clearly derived from a series of meticulous studies. Soares & Shetty (1988) therefore reexamined the issue in current population groups in India to see if the conclusions still held and if so what the basis for the difference in BMR might be. From prospective measurements of BMRs of well-nourished young adult Indian males (18-29+ years) it was evident that the entire group of men had mean BMRs 9.3% lower than those predicted by Schofield's equation for non-Indians. Schofield's predictive equation, derived from age-matched European or North American subjects only, was BMR (in MJ. d-1) = 0.0582 × body weight (kg) + 3.2399 for males aged 18 to 29 years.

However, the percentage deviation of our Indian data from this particular BMR predictive equation of Schofield varied among the subgroups from the same ethnic sample. The better nourished from upper socio-economic groups (in both urban and rural areas) had BMRs only 5.5-5.7% less than the Schofield's European and American values. The age-matched individuals from poor socio-economic groups, who were likely to be undernourished, deviated by 12.7%. When comparisons were made between the measured BMRs and the Schofield predicted BMRs over 5 kg body weight ranges, deviations from the predictive equation showed a curvilinear pattern decreasing to a minimum (<5%) as body weight increased. The deviations were greater again over a range of body weights from <45 to >65 kg (Soares & Shetty, 1988). This pattern was then seen to be true for the BMR data collected over 30 years ago from the same ethnic groups (Niyogi et al., 1939; Sokhey & Malandkar, 1939) when these were reexamined.

In the 55-60 kg body weight range for adults - this being the currently accepted ideal weight for the Reference Indian male for estimating energy requirements (ICMR Expert Group, 1990) - the deviations from those predicted by Schofield's equations were small and amounted to 4.6% in the recent study (Soares & Shetty, 1988) and to 6.8% in the age-matched males of the 1939 study (Niyogi et al., 1939; Sokhey & Malandkar, 1939). The relationship between body weight and BMR is not necessarily one of simple linearity as commented upon earlier by Frans (1981). Nevertheless, the correlations between body weight and BMR are good. The differences may be accounted for by differences in the body composition affecting not only the ratio of fat to fat-free mass (FFM) but also differences in the contribution of muscle and visceral tissues within the FFM (Shetty, 1993).

Similar deviations between the observed Indian data and predicted BMRs, using Schofield's equations, are seen as the BMIs change from BMI 14 to 25. The lowest deviation (-6.5%) is seen in the BMI range of 18-20 which can be considered as normal for India (Soares & Shetty, 1988). Comparisons of three separate data sets of BMR from the Indian subcontinent obtained prospectively in recent years (Srikantia, 1985; McNeil et al., 1987; Soares & Shetty, 1988) not only demonstrate a concurrence between these three sets but also that all three data sets deviate by <2.7% from that predicted by the equations of Henry & Rees (1991) for tropical people. However, the observed BMRs in these three separate sets of data deviate by up to >7.5% from those predicted by Schofield's equations (Soares, Francis & Shetty, 1993).

Climate and environmental factors may account for these 5-6% differences between the observed BMRs of well-nourished Indians and that predicted from equations based on North American or European data. Several reports support the observations that Europeans in tropical climates have BMRs which are about 5% lower than their counterparts in Europe (Mason, 1934; McGregor & Loh, 1941; Munro, 1950; Quenouille et al., 1957). Asians living in the UK have BMRs comparable to their Western counterparts (Mahadeva, 1954; Henry, Piggott & Emery, 1987). A greater degree of muscle relaxation during BMR measurements by Asians may also contribute to the lowered BMR and it is also likely that differences in body composition in the population groups in the tropics may also contribute to this difference.

Low BMIs, BMRs and energy requirements

In a recent publication, Soares & Shetty (1991) compared the BMRs of physically fit, apparently healthy, adult males from two different socio-economic groups. Those from the lower socio-economic groups (in both rural and urban areas) were significantly shorter than individuals from the upper socio-economic groups. Subjects from both socio-economic strata were classified on the basis of BMI into those above a cut-off of 18.5 and those below (Table 1). The absolute BMRs (in MJ per day) were low in all groups with BMI <18.5 as compared to those with BMI >18.5 irrespective of socio-economic status. The BMR corrected for body weight or FFM differences showed that those with BMI <18.5 had significantly higher values than well nourished urban males with BMI >18.5. Low body weight adults and therefore a low BMI have BMRs per kg FFM which are higher than those with a high BMI. This feature of a higher BMR per unit active tissue has been seen earlier in several studies on adults (Kurpad et al., 1989; Piers, Soares & Shetty, 1991), in malnourished school children (Spurr, Reina & Barac-Neito, 1986; Spurr & Reina, 1988) as well as in malnourished patients (Roza & Shizgal, 1984). Srikantia (1985) has also reported a higher BMR per kg body weight in individuals with a low weight-for-height. On the basis of these observations it has been argued that the demonstration of the existence of metabolic efficiency is erroneous on the basis of changes in the index BMR/ kg FFM since these changes may reflect alterations in body composition of the individual (Shetty, 1993). Such a normalization procedure as expressing BMR per unit weight or FFM is now believed to be mathematically biased and invariably leads to the conclusion that individuals with smaller body weights or FFMs have higher BMRs per kg body weight or FFM compared to their heavier counterparts (Ravussin & Bogardus, 1989). Erroneous conclusions regarding such changes can be avoided if a new group's data is subjected to analysis of covariance (ANCOVA) by adjusting for weight or FFM between groups. Soares & Shetty (1991) showed that the CED subjects from lower socio-economic strata with BMI <18.5 have a significantly lower BMR (Table 2) which may be indicative of an apparent increase in 'metabolic economy' of the tissues of these CED individuals (Soares & Shetty, 1991). However, the more interesting feature is that a group of urban individuals from upper socioeconomic strata, with access to adequate energy and protein intake but with BMI <18.5, have BMRs which are lower in absolute terms (Table 1) but show no evidence of an enhanced metabolic economy, unlike the CED subjects with a similar BMI.

Table 1. Comparisons of basal metabolic rates of urban and rural adult males (18-30 years) in India classified by socio-economic status (SES) and body mass index (BMI)





BMR adjusted*

BMR adjusted*



(kJ kg-1d-1)

for weighs

for FFM

Upper socio-economic







Urban (n = 28)






BMI > 18.5



126.0 +

5.92 +

5.86 +

Rural (n = 18)












Urban (n = 24)






Lower socio-economic







Urban (n = 33)






BMI < 18.5




5 49+1,b,c


Rural (n = 22)






All values are mean ± SEM.
*Adjusted by analysis of covariance using weight or FFM, in turn, as covariates.
aStatistically significant as compared to upper SES > 18.5 (urban).
bStatistically significant as compared to upper SES > 18.5 (rural).
cStatistically significant as compared to upper SES < 18.5 (urban).
BMR = basal metabolic rate; FFM = fat-free mass.

Table 2. Comparisons of the measured basal metabolic rate (BMR) of male Indian adults (aged 18-30 years) according to socio-economic status (SES) and chronic energy deficiency (CED) grading


CED Grade*

BMI (kg.m2)

BMR adjusted for FFM† (MJ.d-1)

Upper socio-economic

> 18.5


20.5 ± 0.25

5.85 ± 0.07

Urban & rural (n = 46)

< 18.5

Up to Grade 1

17.8 ± 0.11

5.87 ± 0.09

Urban & rural (n = 29)

Grade 1 to 3

16.2 ± 0.2

5.76 ± 0.11

Lower socio-economic

< 18.5

Up to Grade 1

17.7 ± 0.14

5.41 ± 0 11a,b,c

Urban (n = 33)

Grade 1 to 3

16.0 ± 0.14

5.47 ± 0 10a,b,c

< 18.5

Up to Grade 1

17.5 ± 0.09

5.43 ± 0 12a,b,c

Rural (n = 22)

Grade 1 to 3

16.3 ± 0.16

5.41 ± 0 11a,b,c

All values are mean + SEM.
*Grading of CED based on BMI cut-offs suggested by James et al. (1988).
†Adjusted using ANCOVA with FFM as covariate.
aStatistically significant vs BMI > 18.5 upper SES.
bStatistically significant vs BMI > 17.0 upper SES.
cStatistically significant vs BMI < 17.0 upper SES.
FFM = fat-free mass; BMI = body mass index.

This raises the question whether it is true that all individuals with BMI <18.5 are CED, a matter considered by James et al. (1988) when they proposed the dual classification system for CED using BMI cut-offs and physical activity ratios. Lower limits of acceptable BMIs depend not only on the fat mass and FFM of an individual but also on the level of physical activity which would enhance their energy turnover. The likelihood of thin, tall, physically active adults having a lower than optimum range of BMI and presumably having normal or adequate energy intakes has been recognized since even the NCHS data on adults suggests the presence of a reasonable number of underweight (BMI <18.5) but not necessarily undernourished individuals in a community (Abraham, Johnson & Najjar, 1979).

Using the data of Soares & Shetty (1991), the relationship between body weight and BMR (Fig. 1) and between BMI and BMR (Fig. 2) has been re-examined. It is evident from the two figures that underweight individuals (BMI <18.5) from upper socio-economic strata show identical relationships for both body weight and BMI to those of normal weight (BMI >18.5) from similar socio-economic strata. There appears to be a continuum over the body weight range across the BMI cut-offs used to classify CED. The CED subjects (also with BMI <18.5 but from lower socio-economic strata) have a relationship for weight and BMI to BMR which is distinct and separate from those from the better socio-economic strata. Since the relationships of body weight to BMR would be different in these groups, the BMR predicted would be different too, and thus the estimates of energy requirements would also vary significantly although two individuals may have similar BMIs though not similar body weights. For example, it can be shown, using the relationships predicted from the data of Soares & Shetty (1991), that the energy requirements of a 50 kg moderately active male (with BMI <18.5) will be identical to that predicted by the equation derived from data on well-nourished adults (BMI >18.5) if the male comes from upper socio-economic strata, but in men of the same weight from a low socioeconomic group it will be -12% lower. These observations also strengthen the statement that all individuals with BMI <18.5 need not necessarily be CED on the basis of a simple operational classification more recently proposed for population estimates of numbers of CED (Ferro-Luzzi et al., 1992).

Fig. 1. Relationship of basal metabolic rate (BMR) to body weight. CED = chronic energy deficiency.

Fig. 2. Relationship between basal metabolic rate (BMR) and body mass index (BMI). CED = chronic energy deficiency.

Estimates of energy requirements of a moderately active male calculated using the factorial method from (a) predicted BMR or (b) measured BMR show that the requirements can also be significantly lower if the individual is CED and from lower socio-economic strata compared with an underweight from upper socioeconomic strata, although both have identical BMIs. For instance, the energy requirement of moderately active adult males aged 18-29+ years would be 10.5 MJ per day for a well-nourished person but only 9.0 MJ per day if the individual was CED, although both had the same BMI. It is evident that CED individuals who also have a shorter stature, perhaps as a result of a poor food intake since childhood, will have lower body weights for the same BMI value. Estimates of energy requirements would thus not only be influenced by the BMIs of the individual but also whether the individual with the low BMI is in fact from a poor environment, of shorter stature, is metabolically different and has a different body composition.

BMI, maintenance requirements and allowances for activity in the computation of energy requirements of adults

The Fifth World Survey (FAO, 1985) has proposed that maintenance requirements should be taken as 1.4 × BMR. This incorporates ~3 h of activity while standing and will include washing and dressing activities, but not occupational or socially desirable activities. Table 3 summarizes the maintenance requirements of adults (aged 18-29 years) both males and females, of different stature and of different BMIs within the desirable range of 18.5-25.

Changes in body weight as related to height, i.e. underweight or overweight, can affect the estimates of energy expenditure and hence energy requirement since the BMR, whether predicted or measured, is related to body weight. Under these circumstances deviations of actual body weight from what is 'appropriate' or 'desirable' for that height will influence the estimates of energy requirements although defining the term 'desirable' or 'appropriate' weight-for-height is not easy. A BMI of between 20 and 22 kg/m2 can be considered as being optimal on the basis of the association between life expectancy and BMI in developed countries for both males and females (Bray, 1979). Levels of physical activity (sedentary, moderate or strenuous) will also have to be added to maintenance requirements calculated at 1.4 x BMR. In the case of individuals who tend to be sedentary, allowances have to be made for a minimal level of desirable activity for maintaining physical fitness.

Table 3. Maintenance requirements (MR) (in MJ per day) of males and females of different statures and body mass indices (BMI)





Stature (cm)

Weight (kg)

MR (MJ.d-1)

Weight (kg)

MR (MJd-1)

Weight (kg)

MR (MJ.d-1)













































Maintenance requirements are defined as equivalent to 1.4 × basal metabolic rates of adults aged 18 to 29+ years.

Changes in body weights and stature and their influence on BMI and energy requirements

Longitudinal changes in both body weight and BMI occur in an individual within a population over time and with age. A recent report by Casey et al. (1992), which followed up a group of men and women over a period of 30 or more years, showed that the BMI increased from 21.3 for men and 20.7 for women at age 18 to 27.5 and 26.1 at age 50. Such changes in weight and BMI will alter the energy requirements since increases in body weight will increase BMR. The increased requirement is >10% in both sexes even if the person has a sedentary life style. However, a reduction in BMR with age is also well recognized (Davidson & Passmore, 1966) and this in turn may compensate for the estimated increase in requirements based on body weight. It is thus likely that age-related changes in adulthood also influence the energy requirements of an individual. The changes in the age profile of a population will thereby also change.

Within a population, secular changes in the population profile may also be important. It is widely recognized that in many countries children are growing faster than several decades ago. There are also population changes in adult height, in body weight and BMI. The influence of a higher life expectancy and a lower birth rate will alter the population structure of a country. All these factors will influence the BMR and hence the energy requirements of the population whether of developing or developed countries. James & Schofield (1990) in their manual for planners and nutritionists set out how each of these changes in a community influence the estimates of energy requirements.

Fig. 3. Projected food needs of the Indian population.

Energy requirements of populations are in turn used to estimate national food needs, which in turn have enormous influence on agriculture and food policies. The foods needs of a country can be projected taking into consideration the various allowances recommended by the international consultation (FAO/WHO/UNU, 1985). Figure 3 shows that the food needs of a country such as India estimated for 1985 based simply on current estimated stature, body weights and hence BMIs and level of physical activities of adults and children would be ~30% below the desirable intakes of energy should the general population be of a stature either similar to that of Northern Europe or the smaller affluent subsection within the country who have been and are well fed with statures close to that of NCHS standards. Figure 3 shows how the requirements at current weights are far short of optimum needs should projected increases in adult height and projected increases in adult weight appropriate to height and hence normal range of BMI occur; it also shows how, at current rates of population growth in India, the projected food needs of the Indian population, both based on current body weights and optimum needs, assuming projected increases in body weight, stature and BMIs, can show phenomenal increases which may make remarkable demands on its land and food production.


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James: You are in the unusual position of judging undernutrition on two grounds: one is height, but you do not specify a cut-off point. The other is socio-economic, which could be a problem. Can you distinguish between these? Is the height criterion to be included in a graded way into Grades I-III CED? What is the prevalence of these height or socio-economic categories in your society?

Shetty: Stature is distinctly different in those individuals classified as underweight and those with CED, both having BMI <18.5. The so-called underweights have a normal stature of 1.70-1.74 m, while the undernourished are <1.6m, a clear difference of about 10 cm. We have data from the literature on over 1000 studies over 50 years of normal healthy adults in India. The mean BMI is 19-20 but there are still individuals of <18.5 in these groups of healthy individuals. Many of them have been monitored in a variety of physiological studies and have been assessed as fit, healthy and normal. This may be an effect of age and our cut-off point of 18 years may be too early. Norgan showed that there are correlations in the 30-39 year age group that are very different from the 18-30 year group. The profile of distribution of healthy individuals may change if you eliminate those <20 years of age. Perhaps the inclusion of those <18 years as fully grown is part of the problem.

Waterlow: You indicated that BMR is mostly correlated with weight and BMI adds very little. If you have two people of the same weight, but one is short and stocky and the other is tall and thin and they have different BMIs, will they have the same BMRs? Schofield added height to his weight equations and found that on average it made little difference. But if you compute from his equations individual cases, they are rather different. What is your experience as you seem to think that stature does make a difference to BMR?

Shetty: Their predicted BMRs would be the same, and their measured BMRs would be similar, i.e. within accepted levels of variation. So the shape and the surface area don't seem to make much difference.

James: If you discard height and are left with your socio-economic index you then define these people as healthy because you know they have access to enough food. If that is your method for identifying people with a low BMR, then I am becoming worried. Shetty: The well-fed thin group is small. We need to know what percentage of the population are in this category, and by subtracting them from the calculations we can obtain a better idea of the true numbers in CED. Waterlow: Even in John Durnin's healthy British soldiers there were some <18.5.