Statistical analysis
The baseline characteristics in terms
of demographics, lifestyle, health conditions and female specific
factors were presented as mean (standard
deviation, SD) and numbers
(percentage). General linear models and a chi-square test was used to
compare the differences for baseline characteristics by each parity.
Also, we used general linear model to calculate the estimated values of
FI, ΔKDM-biological age and HD corrected for multiple covariates for
each pairty. A Cox proportional hazards model was performed to calculate
hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause
premature mortality using the “1 production” group as a reference,
with the follow-up time as the time scale. The dose-response
relationship was fexibly modeled by the restricted cubic spline (RCS) to
explore the potential nonlinear correlation between parity and the
hazard of all-cause premature mortality.
We performed stratified analyses by following factors: Born year (<1980
or ≥1980), race/ethnicity (White, Mixed, Asian or Asian British, Black
or Black British, Chinese or Other ethnic group), Townsend deprivation
index (<median or ≥median), BMI (<25, 25–29.9, or ≥30
kg/m2 ), smoking (never, ever, current), Alcohol
intake frequency (<once/mouth,≥once/mouth), hypertension (no or yes),
diabetes (no or yes), hypertension (no or yes), asthma (no or yes),
emphysema and chronic bronchitis (no or yes). To evaluate interactions
between the number of live births and these factors, multiplicative
interaction was assessed by adding interaction terms to the Cox models.
Three sensitivity analyses were
performed. The first analysis excluded the participants whoes follow-up
duration was less than 1 or 2 years, to check if the severe illness
would affect the results. The second analysis evaluated the participants
with additional adjusting for covariates dietary factors (including
fresh fruit intake, dried fruit intake, oily fish intake, salt added to
food, cereal intake, processed meat intake,mineral and other dietary
supplements), to examinte whether dietary factorshad had effect on the
relationship. The third analysis further adjusted covariate biochemical
indicators (including albumin, triglyceride, glucose, LDL, cholesterol,
total bilirubin) for participants, to remove the impact of certain
biochemical indicators unrelated to production on outcomes.
All statistical analyses were conducted by R 4.2.2, and p-values
< 0.05 were considered statistically significant.