Predicting and Preventing Later Death  from Cardiovascular Disease in Adolescents

This week an article in the New England Journal of Medicine  caught my eye:  “Body-Mass Index in 2.3 Million Adolescents and Cardiovascular Death in Adulthood.”   The article groups data on BMI, as measured  from 1967 to 2010 in 2.3 million Israeli adolescents (mean age 17.3),  and correlates  the number of deaths:  1497 from coronary artery disease,  528 from stroke, and 893 from sudden death.  

The authors used BMI (Body Mass Index), normal range 18.5 to 30, underweight, under 18.5;  normal weight 18.5 to 25,  overweight 25 to 30,  obese over 30,  as a predictive measure.  The BMI is calculated by  weight in kilograms /height squared in centimeters.

Those in overweight and obese categories had an increased hazard of dying suddenly or from coronary disease  4.9 times,  stroke 2.6 times , or sudden death  2.1 times, than others in the 40 years of follow-up.

Why Not?

These results got me to thinking – Why not alert adolescents to their risk of death using BMI data when all that is required is their weight and height?  Why not add to weight and height data,   BP levels, and certain lab tests like glucose and blood lipid values?    Why not accompany these data with document stating their relative health and their cardiovascular risk?

Perhaps I was oversimplifying.    But everybody knew, then and now, that obesity,  high blood pressure,  diabetes, and aberrant lipid values  are reliable  prognosticators of future fatal cardiovascular events.   And these measurements  weight, height,  blood glucose, and lipid values are easily obtained during routine visits to a doctor.

Not Optimistic

But I am not  optimistic about the use of routine data as a tool for preventing deaths or changing behavioral lifestyles

In the 1980s,  I developed something  called the “health quotient,”  normal range 80 to 120,  based on the BMI,  blood pressure, and routine blood test values for glucose and lipids.   We measured these things on thousands of patients, mostly non-adolescents,  and sent results to the patients.   We found roughly 30%-35% of  government employees  were pre-diabetic or diabetic,  hypertensive, and had worrisome lipid changes  that often precede heart attacks or stroke or sudden death.

But to little avail.   We learned patients were reluctant to acknowledge their  present and future health problems.  to have their problems  documented as part of their official health records,  or to change their life styles; employers were reluctant to pay for follow up studies;  and physicians were reluctant to heed results or to have the results imposed upon them when  they had not specifically ordered.  

For these and other reasons,  I am reluctant to  overstress the impact that predictive health data might have on the field of “population health,” which is the rage right now in improving the overall health of Americans.  Culture and life style are  difficult things to change, once they have been imbedded in your environment and your approach to health.