Life Course Predictors of Young Men Surviving to Age 90 in a Cohort Study: The Manitoba Follow-up Study.
Robert B TateAudrey U SwiftEdward H ThompsonPhilip D St JohnPublished in: Canadian journal on aging = La revue canadienne du vieillissement (2022)
The purpose of this study was to identify factors at various time points in life that are associated with surviving to age 90. Data from men enrolled in a cohort study since 1948 were considered in 12-year intervals. Logistic regression models were constructed with the outcome of surviving to age 90. Factors were: childhood illness, blood pressure (BP), body mass index (BMI), chronic diseases, and electrocardiogram (ECG) findings. After 1996, the Short Form-36 was added. A total of 3,976 men were born in 1928 or earlier, and hence by the end of our study window in 2018, each had the opportunity of surviving to age 90. Of these, 721 did live to beyond his 90th birthday.The factors in 1948 which predicted surviving were: lower diastolic BP, lower BMI, and not smoking. In 1960, these factors were: lower BP, lower BMI, not smoking, and no major ECG changes. In 1972, these factors were lower BP, not smoking, and fewer disease states. In 1984, these factors were lower systolic BP, not smoking, ECG changes, and fewer disease states. In 1996, the factors were fewer disease states and higher physical and mental health functioning. In 2008, only higher physical functioning predicted survival to the age of 90. In young adulthood, risk factors are important predictors of surviving to age 90; in mid-life, chronic illnesses emerge, and in later life, functional status becomes predominant.
Keyphrases
- body mass index
- blood pressure
- mental health
- risk factors
- physical activity
- heart rate
- smoking cessation
- heart failure
- left ventricular
- heart rate variability
- type diabetes
- adipose tissue
- weight gain
- preterm infants
- wastewater treatment
- big data
- young adults
- hypertensive patients
- insulin resistance
- preterm birth
- gestational age
- mental illness
- deep learning