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alnutrition is a valid concern for healthcare professionals working in long-term care and those working with the elderly. Typically, malnutrition is associated with poor eating, weight loss, and general illness. One of the primary goals in the long-term care of a resident is to avoid weight loss and thereby avoid a malnourished state. Obesity and weight gain are also symptomatic of malnutrition in the elderly.¹ Obesity can be debilitating, impacting residents’ ability to ambulate or propel themselves, limiting social interaction, and leading to isolation and depression.

Statistics

       Nearly 130 million US adults over the age of 20 are overweight with a body mass index (BMI) equal to or greater than 25 (BMI > 25), and more than 61 million adults over the age of 20 are obese (BMI > 30).² The expert panel convened by the National Heart, Lung, and Blood Institute (NHLBI) and the National Institute of Diabetes & Digestive & Kidney Disease (NIDDK) released a report in June 1998 that provided criteria for overweight and obesity similar to those used by the World Health Organization. The panel identified overweight as a BMI > 25–29 and obese as a BMI > 30. These criteria, widely used by the federal government and, increasingly, the broader medical and scientific communities, are based on evidence that health risks increase steeply in individuals with a BMI > 25.
       Baros and iatrike are Greek roots meaning weight and treatment; therefore, “bariatrics” is defined as the treatment of weight. The term “obesity” is defined as an excess storage of body fat, whereas “overweight” is defined as a number compared to a standard. The BMI cutoff points are a guide for definitions of overweight and obesity and are useful for comparative purposes across populations and over time; however, the health risks associated with overweight and obesity are on a continuum and do not necessarily correspond to rigid cutoff points. For example, an overweight individual with a BMI of 29 does not acquire additional health consequences associated with obesity simply by crossing the threshold of BMI > 30. However, health risks generally increase with increasing BMI.

Prevalence and Trends

       The prevalence of overweight and obese adults has steadily increased over the years across genders, ages, racial/ethnic groups, educational levels, and smoking levels. Over the past 40 years, the prevalence of overweight increased from 31.5% to 33.6% in US adults 20 years of age and above. The prevalence of obesity during this same time frame more than doubled from 13.3% to 30.9%. The prevalence of extreme obesity or bariatrics (BMI > 40) increased from 2.9% to 4.7% percent.²
       Life expectancy over the past 100 years has dramatically increased. The average life span of 47.3 years in 1900 rose to 68.2 years in 1950 and 76.9 years in the year 2000.³ Not only are more people living longer, but those surviving past the age of 65 have an increased remaining life expectancy of almost 18 years; for those surviving past the age of 75, it is 11 years. The data from the National Health and Nutrition Examination Survey (NHANES) for 1988–1994 vs. 1999–2000 reveal that the proportion of men who were obese in individuals aged 65–74 increased from 24.1% to 33.4%. For women, the proportion of obese

Figure 1

individuals increased from 26.9% to 38.8%. For men and women, the likelihood of being obese or overweight was greater in those aged 65–74 and less for those 75 years of age and above (see Figure 1).³

Comprehensive Nutrition Assessment

       When conducting a nutrition assessment on a bariatric patient in your facility, the most important findings to look for are comorbid diagnoses (eg, diabetes mellitus, depression, and malnutrition); weight history (if available); body composition (if available); and laboratory values (eg, serum albumin, serum transferring, or serum prealbumin).

Table 1

A common assumption by physicians and other healthcare professionals is that overweight or obese residents are well nourished, when in fact these residents may be under- or malnourished. In a study conducted on frailty in the obese elder, researchers discovered that even though obese elderly individuals have a large amount of fat-free mass (FFM) versus their nonobese counterparts, the fat-free mass expressed relative to body weight (% FFM) was lower.⁵ This indicates that the proportion of muscle to fat is lower, even though these individuals weighed more. This also leads to the conclusion that obesity is an important cause of physical dysfunction in the elderly and that frailty in obese elderly persons is caused by both a smaller relative muscle mass and lower muscle quality.
       Calculating the nutritional needs of the long-term care resident is an important step in the overall assessment process. Unfortunately, there are numerous calculations being used by dietitians, diet technicians, and nutritionists across the country. The most commonly used formula is the Harris-Benedict Equation (HBE), utilizing the actual body weight (ABW), height, and age of the resident. This prediction equation for basal energy expenditure (BEE) was derived from numerous studies of human basal metabolism in the early part of the 20th century and remains the most common method for calculating BEE for clinical and research purposes. The controversy over this calculation is based on the fact that the 1919 study sample was skewed toward young and nonobese persons.⁶

Table 2

       Further research has been conducted on the validity of other established equations for resting metabolic rates in obese and nonobese persons. In one study, resting metabolic rate was measured with indirect calorimetry and then compared to 4 established equations: the Harris-Benedict equation using actual body weight, the Harris-Benedict equation using adjusted body weight in obese individuals, the Owen equation, and the Mifflin-St. Jeor equation.⁷ This study focused on which calculations were outside the + 10% limit from measured values. The researchers found that the Mifflin-St. Jeor equation accurately predicted resting metabolic rate in 78% of all obese and nonobese subjects while the Harris-Benedict equation was only 67% accurate and the Owen equation 65% accurate. Use of ABW in Harris-Benedict calculation seriously underestimated the rate as compared with the use of ABW in subjects with a BMI of > 40. For men with a BMI > 50, the Harris-Benedict calculation failed to predict resting metabolic rate 67% of the time. In women with a BMI > 50, the Owen equation inaccurately predicted resting metabolic rate 92% of the time. The study concluded that the Mifflin-St. Jeor provided the most accurate prediction of resting metabolic rate with the lowest rate of overestimation, especially among the morbidly obese (for whom the magnitude of error was the smallest).⁷
       My own personal research⁸, unpublished but presented at national conferences, found somewhat similar results—in that most calculations are inaccurate—but had a different finding with regard to the most accurate calculation. We compared measured versus calculated methods for determining resting energy expenditure (REE) in obese individuals awaiting bariatric surgery. The average BMI was 39. Four calculation methods were used to predict REE: Harris-Benedict (calculated with 5 different weights); 21 kcal/kg, utilizing ABW; Owen with ABW; and Mifflin St. Jeor with ABW. The data showed that all resting energy equations failed to significantly predict the measured REE—with the exception of the HBE with ABW utilizing [(ABW-IBW) x 25%] + ideal body weight (IBW) from the Metropolitan Life tables and the Hamwi method of calculating IBW.

Table 3

       The researchers found that the Harris-Benedict equation utilizing ABW accurately predicted resting metabolic rate in 93% of all obese subjects while the Owen calculation was 83% accurate, the Mifflin St. Jeor equation 80% accurate, the Harris-Benedict equation using ABW 72% accurate, and the 21 kcal/kg equation 39% accurate.

Conclusion

       With an aging population and the number of overweight and obese persons growing exponentially, it is imperative for professionals to have a greater understanding of the malnourished obese adult. Individual nutritional needs must be considered when developing a care plan that addresses a person’s nutritional status. It is clear that the standard calculations that have been accepted for many years may not provide the most accurate information and may lead to further overfeeding and complications of obesity. In these situations, having the ability to measure energy expenditure would allow for a more accurate care plan. These patients will continue to pose challenges to professionals caring for them—and the better the tools and the more knowledge gained, the better the care provided.