Quetelet was quite a guy. Eknoyan reports that while still a teenager: \”But it was his love of the humanities that dominated his early years. He published poetry, exhibited his paintings, studied sculpture, co-authored the libretto of an opera and translated Byron and Schiller into French.\” At age 23, he was the first recipient of a doctorate in science from the newly founded University of Gent. He became fascinated with probability theory after spending time in Paris with Joseph Fourier (1768–1830), Simeon Poisson (1781–1840) and Pierre Laplace (1749– 1827). He became interested in seeking out probability distributions of the human form, including the creation of the first height-and-weight tables. Eknoyan continues:
His subsequent conceptual evolution in the study of man evolved from the study of averages (physical characteristics), to rates (birth, marriage, growth) and ultimately distributions (around an average, over time, between regions and countries) [12]. The latter was the basis of one of his contributions to statistics; the demonstration that the normal Gaussian distribution, typical throughout nature, applied equally to physical attributes of humans, including body parts, derived from large-scale population studies. …
In developing his index, Quetelet had no interest in obesity. His concern was defining the characteristics of ‘normal man’ and fitting the distribution around the norm. Much like Dublin a century later, he encountered difficulty in fitting the weight to height relationship into a Gaussian curve and began his quest for a solution. In 1831–1832, he conducted what has been considered the first cross-sectional study of newborns and children based on height and weight, and extended it to the study of adults. …
[I]n an 1835 book, A Treatise on Man and the development of his aptitudes, Quetelet wrote: ‘If man increased equally in all dimensions, his weight at different ages would be as the cube of his height. Now, this is not what we really observe. The increase of weight is slower, except during the first year after birth; then the proportion we have just pointed out is pretty regularly observed. But after this period, and until near the age of puberty, weight increases nearly as the square of the height. The development of weight again becomes very rapid at puberty, and almost stops after the twenty-fifth year.\’
Quetelet was famous in his own time, and a major influence on other pioneer statisticians like Francis Galton. A statue of him stands on one corner of the Places des Palais in Brussels, at the entrance to the
Palais des Academies. A century after his death, Belgium put his picture on a postage stamp. But although Quetelet originated the formula, he did not discuss or draw conclusions about obesity.
Big insurance companies began pooling data in quasiprospective collaborative studies around the turn of the century, in which length of life was correlated to a range of risk factors recorded on initial health examinations (Bouk, 2015; Czerniawski, 2007). These intercompany studies were massive, far larger than anything public sector epidemiologists could do at the time. In the landmark Medico‐Actuarial Mortality Investigation (MAMI) of the early teens, over 440,000 insured individuals were examined (representing equal numbers of men and women) for a span of 10–25 years up to 1909—millions of life‐years of observation (Association of Life Insurance Medical Directors & Actuarial Society of America, 1912). MAMI was followed by the similarly designed and executed Medical Impairment Study, which included data on 667,000 men issued policies since 1909, followed through 1928 (Actuarial Society of America & Association of Life Insurance Medical Directors, 1931). Both studies mainly looked at overall mortality rates associated with physical “impairments” and occupations, rarely attempting to identify predictors of particular causes of death (prudently, given the variability in how doctors completed death certificates). Insurance actuaries had tried a number of measures to gauge obesity such as girth for spine length, but the statisticians found that weight for height had the best predictive power for longevity (Czerniawski, 2007; Marks, 1956). And the association between weight and mortality was strong and consistent, changing very little between the generations represented by the two big studies (for people older than 25). In the Medical Impairment Study, for example, men categorized as 25% or more above average weight for their height suffered 30–40% higher mortality rates (depending on age). Similar findings were reported for women, although the mortality penalties of high weight were not quite as severe (Marks, 1956).By 1900, insurance firms were already screening out applicants well above or below the average weight for their height and, unsurprisingly, after the big intercompany studies, the firms revised their rates and standard height‐weight tables to reflect greater mortality penalties for overweight (and smaller mortality penalties for underweight, as tuberculosis was in retreat). Tables of a normal or healthy weight for each height category were widely distributed by insurance companies and ubiquitous in doctors’ offices during the early 20th century (Weigley, 1984). Thus, the insurance industry informed the understanding of proper body weight among doctors and patients alike, during the period when it first became a matter of popular concern (evidenced, for instance, by rapid diffusion of weighing scales; Jutel, 2001). …Life insurance firms stiffened their price discrimination; that is, the overweight paid more for their “substandard” policies, if they could get them at all (Czerniawski, 2007; Weigley, 1984). Later, by 1930s, it was something like a universally accepted medical fact that obesity contributed to early death, especially from heart disease. …
Keys led a famous \”Seven Countries\” study that looked at how obesity might predict coronary heart disease, and when the study was published in 1972, it included three measure of obesity: skinfold measures, weight-for-height, and what Rasmussen calls \”a heretofore obscure measure—BMI (weight in kilograms divided by height in meters squared, first proposed a century earlier by Quetelet).\” The statistics suggested that the skinfold measures offered no difference in predictive power over the weight measures: \”So at this point, after more than 20 years of conspicuous efforts to showcase skinfold and the body fatness it measured as a more rigorously scientific and predictively effective index of obesity than relative weight, Keys just dropped the topic of skinfold and adiposity and embraced BMI …\” However, in his study, BMI had only a very mixed record in predicting coronary heart disease.