Summary
Chronoepidemiology studies alterations of biologic rhythms with several frequencies as harbingers and possibly determinants of the risk of developing certain diseases, such as breast cancer and high blood pressure, among others. For such risk assessment, a small number of selected clinically healthy women of three age groups was extensively sampled in two geographic locations. Data on twelve plasma hormones in addition to those on some systemic variables, including breast surface temperature, heart rate and blood pressure, determined around the clock and along the calendar, are here analyzed further. The risk of developing certain diseases was assessed by epidemiologically designed questionnaires. Such data revealed correlations of the familial risk of developing breast cancer with the circannual amplitudes of circulating prolactin and TSH. These indices are costly in labor, other resources and time; it takes at least a year and quite a few samples to estimate circannual rhythms reliably.
In the attempt to reduce sampling requirements to one or at most two plasma samples, a chronobiologic pattern discrimination analysis was undertaken on the original data from the subgroup of young adult women. Data were normalized by the sample standard deviation of each variable and processed according to proximity (so-called nearest-neighbor) rules, for dimension reduction and classification. For each variable, each subject’s samples were classified by reference to those of all others, in a so-called monotest, superceding an earlier stepwise implemented polytest. The latter does not compare the contribution of each variable (or combination of variables) at each sampling time in relation to that of all others, whereas the monotest, an all-subsets variable selection technique, does so. The monotest results objectively identify certain endocrine variables at specified times for further testing. The number of variables is smaller than that of variables identified by the polytest for the same purpose of risk assessment. This added dimension reduction, beyond that achieved by the polytest, should facilitate follow-up tests on larger, properly stratified and randomized cohorts followed preferably for a life time. The computer method of pattern discrimination here used is illustrated for broader applications in chronoepidemiology.
With respect to the risk of developing breast cancer, pattern discrimination not only singles out plasma insulin, T3 and T4 as the primary classifiers, but shows further the circannual— and circadian-stage dependence of the classification. The best classifiers identified by monotest differ among seasons. The total number of variables used for classification is reduced from 12 to 7, rather than to 11 as in the case of the polytest. The total number of hormones specified for further testing as harbingers in any one season does not exceed three. In some seasons the classifying constellation of variables includes the same hormone, yet the recommended clock hour(s) of sampling differs considerably in different seasons. Different classifiers and different corresponding reference values from variables that undergo circadian and circannual rhythms now await literally and figuratively the test of time. Follow-up work is indispensable, beyond the particular variables here studied for a limited purpose, namely in the chronoepidemiology of human breast cancer. It will be more economical to seek time-specified reference values for the assessment of neuroendocrine aspects of the time-dependent (chrono-) risk of developing multiple, some times competing diseases, including a variety of cancers.
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Hermida, R.C., Halberg, F. (1989). Assessment of the Endocrine Risk of Developing Breast Cancer. In: Levine, A.S. (eds) Etiology of Cancer in Man. Cancer Growth and Progression, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2532-8_11
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