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      Factors influencing lung function: are the predicted values for spirometry reliable enough?

      Journal of physiology and pharmacology : an official journal of the Polish Physiological Society
      Aging, physiology, Antioxidants, Diabetes Mellitus, physiopathology, Diet, Forced Expiratory Volume, genetics, Heart Failure, Humans, Hyperglycemia, Hypertension, Myocardial Infarction, Reference Values, Spirometry, Vital Capacity

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          Abstract

          Spirometric lung function parameters are used as a diagnostic tool and to monitor therapy effectiveness or the course of disease. On the other hand, forced expiratory volume in one second (FEV(1)) and forced vital capacity (FVC) are important predictors of morbidity and mortality in elderly persons. In clinical use, FEV(1) and FVC are measured in liters and usually each is expressed as a percentage of the predicted value. Reference values used for the prediction of lung function should be reliable. It seems crucial that the reference cohort be representative. There is no doubt that gender and height are the most important predictors of lung function. The third predictor, age, may be a confounding factor. The study of age-dependent changes in lung function through the lifespan reveals distinctive differences. The FEV(1) and FVC in adults are related to the maximum level attained, the plateau period, and the rate of lung function decline. A non-linear dependence between age and lung function parameters is more complex. The maximum level of lung function, possible to attain, is influenced by a genetic factor. The plateau and decline phases are closely connected with several independent predictors. In the last decade, some new factors influencing lung function have been established. A relation between lung function and hyperglycemia of diabetes mellitus is a novel field of interest. Also, the influence on lung function of waist size, weight, and body composition or muscle strength are underscored. These, previously not full well unrecognized, factors make it difficult to get accurate norms with regression equations, traditionally using sex, height, and age as predictors.

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