Much of what we eat and drink play central roles in modelling the body's ability to gain excess weight and to lose it. Low calorie diets have been approved to be the best way to lose weight. Several studies show that low-fat diets often don't work, in part because these diets replace fat with easily digested carbohydrates. We are what we eat. Diets that help people take in fewer calories would be effective for body weight control purposes. The best diet would be the one that is good for all parts of the body, and not precisely the one which is strictly recommended for waistline expectations. Because the current thinking regarding weight loss is to burn far more calories than consumed, dietary supplement production firms rely on our beliefs in this delineation, in order to sell more inventory.
Obesity is a complex condition having serious social and psychological dimensions. The health consequences associated with obesity like osteoarthritis, type 2 diabetes and hypertension reduce the overall quality of life and induce disability in adults. The body mass index (BMI), which is defined as body weight divided by height squared (kg/m2) is now more widely used in clinical settings to diagnose excess adiposity and underweight status. Because BMI is a surrogate of body fat, it has become the variable used to define standards of overweight and obesity. The BMI range of 18.5 – 24.9 is often viewed as the range of ideal or healthy body weights. The WHO standard defines a pre-obese state (over weight) as BMI between 25 and 29.9 kg/m2 and obesity as BMI ≥30 kg/m2.
Many treatment modalities use body weights which are estimated from descriptors such as: the gender, the total body weight (TBW) and the height (Ht). This is because, these body weights reduce the dosing error which is associated with using the total body weight of critically ill patients. Predicted body weights are assumed to confer information regarding a number of variables which ensure a correct estimation of the dose of treatment which is to be administered to the patient. This reduces the risks that are associated with overdosing and underdosing. TBW may be applicable in patients with a normal body mass index (BMI), but may not be appropriate in the obese patients, due to a relative increase in the proportion of the fat-compartment. This fat is expressed as the BFP (Body Fat Percentage) or %Fat.
The dose of a drug is determined by the plasma concentration that is required to achieve the desired effect. The plasma concentration of a drug following administration is dependent on its absorption (if not administered via the intravenous route), distribution, metabolism and excretion from the body. The duration of administration will also affect the plasma concentration of a drug. In individuals with normal body weight (BMI <30), it is generally accepted that the use of total body weight (TBW) ensures satisfactory drug dosing. In obese individuals, the question of which weight to use is more complex as the fatty compartment represents a large proportion of their body weight. The correct weight to use when modelling obese individuals remains a hotly debated topic among clinical pharmacologists.
We apply an easy-to-use linear equation to calculate both ideal body weight (IBW) and body weight (Wt) at any target BMI value. The advantage of IBW equations is that they predict weight as a linear function of height, where corporal height (Ht) is in inches (in) and Ht is ≥60 inches. For the first time to our knowledge, we unify the concepts of the IBW equation and the BMI to define target body weights. We show that a single linear equation can estimate both the IBW and target body weight (Wt) for any BMI and height. In the process, we show that the advantages of the IBW equation and BMI can be combined into a single easy to use equation. This easy-to-remember formula provides a highly accurate prediction of body weight (Wt) at any target BMI and height. Moreover, our equation is applicable in all genders.
From the several weight-height indices, the body mass index (BMI) seems to be the most appropriate, because its correlation with the body fat percentage (BFP) is high, and its correlation with body height is low. Obesity is characterized by an increased amount of body fat. In young adults, this condition is defined as having body fat >25% in males, and >35% in females. From a physiological point of view, it is not the degree of excess weight (i.e., measured by e.g., the BMI), but the degree of body fat that is estimated as a factor and indicator of risk. Overweight does not necessarily coincide with an excess of body fat. The percentage of body fat (%Fat) is not just a linear function of BMI, but also a function of age and age-by-BMI interaction. Here, the relationship between BMI and body fat is age- and gender-dependent.
The LBM (Lean Body Mass) is derived by subtracting the body fat weight from the total body weight (TBW). LBM has been described as an index superior to TBW for prescribing proper levels of medication and for assessing metabolic disorders, as body fat is less relevant for metabolism. Boer's formula [ref № 13] is the method of choice for estimating LBM when calculating the dose given in contrast CT in obese individuals with a BMI between 35 and 40. The ideal body weight (IBW) could also serve as surrogate for LBM. An alternative dosing approach is to use IBW plus a multiplier of the difference between TBW and IBW to derive an adjusted body weight (AjBW) that is intermediate between IBW and TBW. AjBW was developed as a size descriptor for use in pharmacokinetic tests.
Obesity increases cardiovascular risk through risk factors such as, increased fasting plasma triglycerides, high LDL cholesterol, low HDL cholesterol, elevated blood glucose and insulin levels and high blood pressure. Lipid-dependent metabolic risk factors associated to obesity are: the presence of the small dense LDL phenotype, postprandial hyperlipidemia with accumulation of atherogenic remnants and hepatic overproduction of apo B containing lipoproteins. Here are typical features of the metabolic syndrome which may be associated to a pro-inflammatory gradient. For a differentiation between liver and heart diseases — the HBDH/LDH ratio should be calculated. A ratio >0.9 can be measured in myocardial infarction while a ratio <0.6 indicates liver diseases which could be parenchymal.