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predm_mcps.R
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# Prediabetes as a risk factor for metabolic disease and
# cause-specific mortality in 125,442 diabetes-free adults in Mexico City
# Data Analysis: Omar Yaxmehen Bello-Chavolla & Carlos A. Fermin-Martinez
# Latest version of Analysis February 2024
# For any question regarding analysis contact:
# Omar Yaxmehen Bello-Chavolla at [email protected]
#### Library------------------------ ####
pacman::p_load(
haven, tidyverse, data.table, ggpubr, survival, flexsurv, coxme, coxphw,
flextable, gtsummary, officer, pROC, timeROC, survivalROC, lmtest, nortest,
jtools, viridis, forestplot, forestmodel, forestploterm, PropCIs, Epi, popEpi,
caret, glmnet, bestNormalize, blandr, BlandAltmanLeh, corrplot, rms, dummy,
ggalluvial, ggsankey, ggbreak, ggedit, ggimage, ggsci, ggplotify, gtools,
survminer, magrittr, foreach, doParallel, patchwork, lubridate, naniar,
circlize, shadowtext, khroma, FactoMineR, fpc, NbClust, cluster, UpSetR,
TimeVTree, simPH, factoextra, gridExtra, nhanesA, wesanderson, chorddiag)
#### Data set management------------ ####
#Official working directories:
setwd("~/OneDrive - UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO/Mexico City Cohort Study")
#setwd("/Users/carlosfermin/Library/CloudStorage/OneDrive-UNIVERSIDADNACIONALAUTÓNOMADEMÉXICO/Mexico City Cohort Study")
### Load dataset ###
mcps<-read.csv("Data/2021-004 MCPS BASELINE.csv") %>%
left_join(read.csv("Data/2022-012 MCPS MORTALITY.csv"), by="PATID") %>%
left_join(read.csv("Data/2022-012 MCPS RESURVEY.csv"), by="PATID") %>%
left_join(read.csv("Data/2022-012 MCPS BASELINE_NMR_FULLCOHORT.csv"), by="PATID")
### Recode dataset ###
#Diabetes
mcps$diabetes <- with(mcps, ifelse((
BASE_HBA1C>=6.5|BASE_DIABETES==1|DRUG_D1==1|DRUG_D2==1|DRUG_D3==1|DRUG_D4==1), 1, 0))
#Prediabetes
mcps$predm<- ifelse(mcps$BASE_HBA1C>=5.7, 1,0) #ADA
mcps$predm_ice<- ifelse(mcps$BASE_HBA1C>=6.0,1,0) #IEC
mcps$predm[mcps$diabetes==1] <- 0; mcps$predm_ice[mcps$diabetes==1] <- 0
mcps$pred_ADA <- mcps$predm; mcps$pred_IEC <- mcps$predm_ice
#HbA1C categories
mcps$hba1c_categories <- mcps$BASE_HBA1C %>% cut(c(-Inf, 5.7, 6, Inf), right = F) %>%
factor(labels = c("<5.7%", "5.7-5.9%", "6.0-6.4%")); mcps$hba1c_cat<- mcps$hba1c_categories
#Follow-up
mcps <- mcps %>% mutate("date_recruited" = decimal_date(ym(paste0(YEAR_RECRUITED, MONTH_RECRUITED))))
mcps$age_risk<-mcps$AGE+mcps$PERSON_YEARS
#Deaths
#All cause
mcps$D000_p<-ifelse(mcps$D000==1 & mcps$age_risk<75,1,0); mcps$D000_o<-ifelse(mcps$D000==1 & mcps$age_risk>=75,1,0)
mcps$D000_p5<-ifelse(mcps$D000_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D000_o5<-ifelse(mcps$D000_o==1 & mcps$PERSON_YEARS>5,1,0)
#Cardiac
mcps$D003_p<-ifelse(mcps$D003==1 & mcps$age_risk<75,1,0); mcps$D003_o<-ifelse(mcps$D003==1 & mcps$age_risk>=75,1,0)
mcps$D003_p5<-ifelse(mcps$D003_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D003_o5<-ifelse(mcps$D003_o==1 & mcps$PERSON_YEARS>5,1,0)
#Cerebrovascular
mcps$D008_p<-ifelse(mcps$D008==1 & mcps$age_risk<75,1,0); mcps$D008_o<-ifelse(mcps$D008==1 & mcps$age_risk>=75,1,0)
mcps$D008_p5<-ifelse(mcps$D008_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D008_o5<-ifelse(mcps$D008_o==1 & mcps$PERSON_YEARS>5,1,0)
#Other vascular
mcps$D014_p<-ifelse(mcps$D014==1 & mcps$age_risk<75,1,0); mcps$D014_o<-ifelse(mcps$D014==1 & mcps$age_risk>=75,1,0)
mcps$D014_p5<-ifelse(mcps$D014_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D014_o5<-ifelse(mcps$D014_o==1 & mcps$PERSON_YEARS>5,1,0)
#Renal
mcps$D019_p<-ifelse(mcps$D019==1 & mcps$age_risk<75,1,0); mcps$D019_o<-ifelse(mcps$D019==1 & mcps$age_risk>=75,1,0)
mcps$D019_p5<-ifelse(mcps$D019_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D019_o5<-ifelse(mcps$D019_o==1 & mcps$PERSON_YEARS>5,1,0)
#Acute diabetic
mcps$D023_p<-ifelse(mcps$D023==1 & mcps$age_risk<75,1,0); mcps$D023_o<-ifelse(mcps$D023==1 & mcps$age_risk>=75,1,0)
mcps$D023_p5<-ifelse(mcps$D023_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D023_o5<-ifelse(mcps$D023_o==1 & mcps$PERSON_YEARS>5,1,0)
#Hepatobiliary
mcps$D022_p<-ifelse(mcps$D022==1 & mcps$age_risk<75,1,0); mcps$D022_o<-ifelse(mcps$D022==1 & mcps$age_risk>=75,1,0)
mcps$D022_p5<-ifelse(mcps$D022_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D022_o5<-ifelse(mcps$D022_o==1 & mcps$PERSON_YEARS>5,1,0)
#Other GI
mcps$D045_p<-ifelse(mcps$D045==1 & mcps$age_risk<75,1,0); mcps$D045_o<-ifelse(mcps$D045==1 & mcps$age_risk>=75,1,0)
mcps$D045_p5<-ifelse(mcps$D045_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D045_o5<-ifelse(mcps$D045_o==1 & mcps$PERSON_YEARS>5,1,0)
#Neoplastic
mcps$D044_p<-ifelse(mcps$D044==1 & mcps$age_risk<75,1,0); mcps$D044_o<-ifelse(mcps$D044==1 & mcps$age_risk>=75,1,0)
mcps$D044_p5<-ifelse(mcps$D044_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D044_o5<-ifelse(mcps$D044_o==1 & mcps$PERSON_YEARS>5,1,0)
#Respiratory
mcps$D040_p<-ifelse(mcps$D040==1 & mcps$age_risk<75,1,0); mcps$D040_o<-ifelse(mcps$D040==1 & mcps$age_risk>=75,1,0)
mcps$D040_p5<-ifelse(mcps$D040_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$D040_o5<-ifelse(mcps$D040_o==1 & mcps$PERSON_YEARS>5,1,0)
#External, ill-defined, other
mcps$ext_ill <- with(mcps, ifelse((D000+D003+D008+D014+D019+D023+D022+D045+D044+D040)==1, 1, 0))
mcps$ext_ill_p<-ifelse(mcps$ext_ill==1 & mcps$age_risk<75,1,0); mcps$ext_ill_o<-ifelse(mcps$ext_ill==1 & mcps$age_risk>=75,1,0)
mcps$ext_ill_p5<-ifelse(mcps$ext_ill_p==1 & mcps$PERSON_YEARS>5,1,0); mcps$ext_ill_o5<-ifelse(mcps$ext_ill_o==1 & mcps$PERSON_YEARS>5,1,0)
#Sex
mcps$Sex<-ifelse( mcps$MALE==1, 0, 1)
mcps$female<-ifelse( mcps$MALE==0, 1, 0)
#Age
mcps$edad60<-ifelse( mcps$AGE>=60, 1, 0)
mcps$edad60<-factor( mcps$edad60, labels = c("<60 years", "≥60 years"))
#Alcohol Intake
mcps$alcohol<- mcps$ALCGP; mcps$alcohol[ mcps$ALCGP %in% c(3:5)]<-3
mcps$alcohol<-factor( mcps$alcohol,labels = c("Never","Former","Current"))
#Smoking
mcps$smoke<- mcps$SMOKEGP; mcps$smoke[ mcps$SMOKEGP %in% c(3:5)]<-3
mcps$smoke<-factor( mcps$smoke,labels = c("Never", "Former","Current"))
#Physical Activity
mcps$PHYSGP<-factor( mcps$PHYSGP,labels = c("None", "≤2 times a week","≥3 times a week"))
mcps$phys<-ifelse(mcps$PHYSGP=="None",0,1)
#Others
mcps$BMI<- mcps$WEIGHT/(( mcps$HEIGHT/100)^2)
mcps$ICE<- mcps$WAISTC/( mcps$HEIGHT)
mcps$R_HXDIAB[is.na( mcps$R_HXDIAB)]<-0
mcps$ASCVD<-ifelse( mcps$D003==1 | mcps$D008==1, 1, 0)
mcps$college<-ifelse(mcps$EDU_LEVEL==1,1,0)
#Labels
setattr( mcps$AGE, "label", "Age, (Years)")
setattr( mcps$female, "label", "Female sex, (%)")
setattr( mcps$COYOACAN, "label", "Residence in Coyoacan, (%)")
setattr( mcps$EDU_LEVEL, "label", "Educationnal Attainments, (%)")
setattr( mcps$INCOME, "label", "Income, (pesos/month)")
setattr( mcps$smoke, "label", "Smoking, (%)")
setattr( mcps$alcohol, "label", "Alcohol intake, (%)")
setattr( mcps$PHYSGP, "label", "Leisure-time physical activity, (%)")
setattr( mcps$phys, "label", "Regular leisure-time physical activity, (%)")
setattr( mcps$BASE_DIABETES, "label", "Diabetes, (%)")
setattr( mcps$BASE_HYPERTENSION, "label", "Hypertension, (%)")
setattr( mcps$BASE_STROKE, "label", "Stroke, (%)")
setattr( mcps$BASE_CKD, "label", "CKD, (%)")
setattr( mcps$BASE_HEARTATTACK, "label", "Myocardial infarction, (%)")
setattr( mcps$BMI, "label", "Body Mass Index, (kg/m2)")
setattr( mcps$ICE, "label", "Waist-to-Height Ratio")
setattr( mcps$HIPC, "label", "Hip Circunference, (cm)")
setattr( mcps$SBP1, "label", "Systolic Blood Pressure, (mmHg)")
setattr( mcps$DBP1, "label", "Diastolic Blood Pressure, (mmHg)")
setattr( mcps$BASE_HBA1C, "label", "HbA1c, (%)")
setattr( mcps$predm, "label", "HbA1c ≥5.7%")
setattr( mcps$predm_ice, "label", "HbA1c ≥6.0%")
setattr( mcps$edad60, "label", "Age ≥60")
setattr( mcps$college, "label", "University/college educated (%)")
setattr( mcps$Clinical_LDL_C, "label", "LDL cholesterol, mmol/L")
setattr( mcps$HDL_C, "label", "HDL cholesterol, mmol/L")
setattr( mcps$Total_TG, "label", "Triglycerides, mmol/L")
setattr( mcps$ApoA1, "label", "Apolipoprotein A1, mmol/L")
setattr( mcps$ApoB, "label", "Apolipoprotein B, mmol/L")
### Filter dataset ###
#Filter 1: diagnosed or undiagnosed diabetes
mcps1 <- mcps %>% filter(
BASE_DIABETES==0&DRUG_D1==0&DRUG_D2==0&DRUG_D3==0&DRUG_D4==0) %>%
filter(BASE_HBA1C<6.5|is.na(BASE_HBA1C))
#Filter 2: missing data on HbA1c, covariates and mortality
mcps2 <- mcps1 %>% filter(
!is.na(D000), !is.na(BASE_HBA1C),
!is.na(MALE), !is.na(AGE),!is.na(COYOACAN), !is.na(EDU_LEVEL),
!is.na(SMOKEGP), !is.na(ALCGP), !is.na(PHYSGP), !is.na(BMI), !is.na(WAISTC),
!is.na(SBP1), !is.na(DBP1), !is.na(Clinical_LDL_C), !is.na(HDL_C),
!is.na(Total_TG), !is.na(ApoA1), !is.na(ApoB))
#Filter 3: comorbidities (IHD, stroke, cancer, cirrhosis, COPD, CKD)
mcps3 <- mcps2 %>%
filter(BASE_HEARTATTACK==0 &
BASE_ANGINA==0 &
BASE_STROKE==0 &
BASE_BREASTCANCER==0 &
BASE_LUNGCANCER==0 &
BASE_STOMCANCER==0 &
BASE_ORALCANCER==0 &
BASE_PROSTATECANCER==0 &
BASE_CERVCANCER==0 &
BASE_OTHCANCER==0 &
BASE_EMPHYSEMA==0 &
BASE_CKD==0 &
BASE_CIRR==0)
#Filter 4: aged >85 years old
mcps4 <- mcps3 %>% filter(AGE<=84)
### Population flowchart ###
nrow(mcps) #Total
nrow(mcps)-nrow(mcps1) #Filter 1: Diabetes
nrow(mcps1) #Diabetes-free
nrow(mcps1)-nrow(mcps2) #Filter 2: Missing data
nrow(mcps2) #Complete data
nrow(mcps2)-nrow(mcps3) #Filter 3: Comorbidities
nrow(mcps3) #Without comorbidities
nrow(mcps3)-nrow(mcps4) #Filter 4: Aged >84 years
nrow(mcps4) #35-84 years old
### Manuscript overview ###
(nrow(mcps) - nrow(mcps4)) #Total number of participants removed
((nrow(mcps) - nrow(mcps4))/nrow(mcps)*100) %>% round(1) #Percentage of participants removed
nrow(mcps)-nrow(mcps1); ((nrow(mcps)-nrow(mcps1))/nrow(mcps)*100) %>% round(1) #Diabetes
nrow(mcps1)-nrow(mcps2); ((nrow(mcps1)-nrow(mcps2))/nrow(mcps)*100) %>% round(1) #Missing data
nrow(mcps2)-nrow(mcps3); ((nrow(mcps2)-nrow(mcps3))/nrow(mcps)*100) %>% round(1) #Comorbidities
nrow(mcps3)-nrow(mcps4); ((nrow(mcps3)-nrow(mcps4))/nrow(mcps)*100) %>% round(1) #Aged >95 years
nrow(mcps)-nrow(mcps) #Duplicated
nrow(mcps4) #Final sample size
(mcps4$AGE>=75) %>% table #Aged ≥75 total
((mcps4$AGE>=75) %>% table %>% prop.table*100) %>% round(1) #Aged ≥75 percentage
(with(mcps4, age_risk>=75) %>% table) #Aged ≥75 at the end of follow-up
(with(mcps4, age_risk>=75) %>% table) %>% prop.table() %>% round(3)*100 #Percentage
mcps4$hba1c_categories %>% table #A1c groups total
(mcps4$hba1c_categories %>% table %>% prop.table*100) %>% round(1) #A1c groups pecentage
mcps_main <- mcps4 %>% filter(AGE<75); nrow(mcps_main)
(mcps_main$pred_ADA %>% table)[2]; (mcps_main$pred_IEC %>% table)[2] #Prediabetes definitions
((mcps_main$pred_ADA %>% table %>% prop.table)[2]*100) %>% round(1)
((mcps_main$pred_IEC %>% table %>% prop.table)[2]*100) %>% round(1)
remove(mcps_main)
### Rename datasets ###
mcps_fin <- mcps4 #MAIN ANALYSIS
mcps_sens <- mcps2 #SENSITIVITY ANALYSIS (with comorbidities)
remove(mcps1, mcps2, mcps3)
### Recodify ###
mcps_sens$n_comorb <- mcps_sens %>% dplyr::select(
BASE_HEARTATTACK, BASE_ANGINA, BASE_STROKE, BASE_BREASTCANCER,
BASE_LUNGCANCER, BASE_STOMCANCER, BASE_ORALCANCER, BASE_CERVCANCER,
BASE_PROSTATECANCER, BASE_OTHCANCER, BASE_EMPHYSEMA, BASE_CKD,
BASE_CIRR, BASE_PAD,BASE_ASTHMA, BASE_PEP) %>% apply(1, sum)
### Death comparison ###
### Follow-up
quantile(mcps_fin$PERSON_YEARS) %>% round(1)
#Without comorbidities
death.distribution <- data.frame(
"All"=mcps_fin %>% summarise(
"All"=sum(D000),"Cardiac"=sum(D003),"Cerebrovascular"=sum(D008),"Other_vascular"=sum(D014),"Renal"=sum(D019),
"Cardiovascular"=sum(D003)+sum(D008)+sum(D014),
"Acute_diabetic"=sum(D023), "Hepatobiliary"=sum(D022), "Other_GI"=sum(D045),
"Neoplastic"=sum(D040),"Respiratory"=sum(D044), "External_ill"=sum(ext_ill)) %>% t,
"Premature"=mcps_fin %>% summarise(
"All"=sum(D000_p),"Cardiac"=sum(D003_p),"Cerebrovascular"=sum(D008_p),"Other_vascular"=sum(D014_p),"Renal"=sum(D019_p),
"Cardiovascular"=sum(D003_p)+sum(D008_p)+sum(D014_p),
"Acute_diabetic"=sum(D023_p),"Hepatobiliary"=sum(D022_p), "Other_GI"=sum(D045_p),
"Neoplastic"=sum(D040_p),"Respiratory"=sum(D044_p), "External_ill"=sum(ext_ill_p)) %>% t,
"Old"=mcps_fin %>% summarise(
"All"=sum(D000_o),"Cardiac"=sum(D003_o),"Cerebrovascular"=sum(D008_o),"Other_vascular"=sum(D014_o),"Renal"=sum(D019_o),
"Cardiovascular"=sum(D003_o)+sum(D008_o)+sum(D014_o),
"Acute_diabetic"=sum(D023_o),"Hepatobiliary"=sum(D022_o), "Other_GI"=sum(D045_o),
"Neoplastic"=sum(D040_o),"Respiratory"=sum(D044_o), "External_ill"=sum(ext_ill_o)) %>% t); death.distribution
(death.distribution[,2]*100/death.distribution[1,2]) %>% round(1) #<75
(death.distribution[,3]*100/death.distribution[1,3]) %>% round(1) #≥75
9260-5333
6495-2568
#Follow-up
mcps_fin$PERSON_YEARS %>% sum
mcps_fin$PERSON_YEARS %>% summary %>% round(1)
#Number of deaths with and without prediabetes
rbind( #ADA
(mcps_fin %>% group_by(predm) %>% summarise("D"=sum(D000),"Prop"=sum(D000)/sum(!predm)))[1,],
(mcps_fin %>% group_by(predm) %>% summarise("D"=sum(D000),"Prop"=sum(D000)/sum(predm)))[2,])
rbind( #IEC
(mcps_fin %>% group_by(predm_ice) %>% summarise("D"=sum(D000),"Prop"=sum(D000)/sum(!predm_ice)))[1,],
(mcps_fin %>% group_by(predm_ice) %>% summarise("D"=sum(D000),"Prop"=sum(D000)/sum(predm_ice)))[2,])
#With comorbidities
data.frame(
"All"=mcps_sens %>% summarise(
"All"=sum(D000),"Cardiac"=sum(D003),"Cerebrovascular"=sum(D008),"Other_vascular"=sum(D014),"Renal"=sum(D019),
"Acute_diabetic"=sum(D023), "Hepatobiliary"=sum(D022), "Other_GI"=sum(D045),
"Neoplastic"=sum(D040),"Respiratory"=sum(D044), "External_ill"=sum(ext_ill)) %>% t,
"Premature"=mcps_sens %>% summarise(
"All"=sum(D000_p5),"Cardiac"=sum(D003_p5),"Cerebrovascular"=sum(D008_p5),"Other_vascular"=sum(D014_p5),"Renal"=sum(D019_p5),
"Acute_diabetic"=sum(D023_p5),"Hepatobiliary"=sum(D022_p5), "Other_GI"=sum(D045_p5),
"Neoplastic"=sum(D040_p5),"Respiratory"=sum(D044_p5), "External_ill"=sum(ext_ill_p5)) %>% t,
"Old"=mcps_sens %>% summarise(
"All"=sum(D000_o5),"Cardiac"=sum(D003_o5),"Cerebrovascular"=sum(D008_o5),"Other_vascular"=sum(D014_o5),"Renal"=sum(D019_o5),
"Acute_diabetic"=sum(D023_o5),"Hepatobiliary"=sum(D022_o5), "Other_GI"=sum(D045_o5),
"Neoplastic"=sum(D040_o5),"Respiratory"=sum(D044_o5), "External_ill"=sum(ext_ill_o5)) %>% t)
#At-risk participants
(with(mcps_fin, age_risk<75) %>% table)[2] #At-risk for premature
(with(mcps_fin, age_risk>=75) %>% table)[2] #At risk for older
(with(mcps_sens, age_risk<75) %>% table)[2] #At-risk for premature with comorb
#Deaths occur disproportionally higher in men for cardiac and hepatobiliary causes
(mcps_fin %>% group_by(MALE) %>% summarise(
A1=sum(D000_p)/sum(PERSON_YEARS), A2=sum(D003_p)/sum(PERSON_YEARS), A3=sum(D008_p)/sum(PERSON_YEARS),
A4=sum(D014_p)/sum(PERSON_YEARS), A5=sum(D019_p)/sum(PERSON_YEARS), A6=sum(D023_p)/sum(PERSON_YEARS),
A7=sum(D022_p)/sum(PERSON_YEARS), A8=sum(D045_p)/sum(PERSON_YEARS), A9=sum(D044_p)/sum(PERSON_YEARS),
A10=sum(D040_p)/sum(PERSON_YEARS), A11=sum(ext_ill_p)/sum(PERSON_YEARS))*100)->GGG
GGG[2,]/GGG[1,]
#### Table 1: Study population----- ####
mcps_fin$icc<-mcps_fin$WAISTC/mcps_fin$HIPC
setattr(mcps_fin$icc, "label", "Waist-Hip Ratio")
tab1 <- mcps_fin %>% filter(AGE<75) %>%
dplyr::select(hba1c_categories,AGE,BASE_HBA1C,female,
COYOACAN,college,smoke,alcohol,phys,
BMI,ICE,icc,SBP1,DBP1,
Clinical_LDL_C,HDL_C,Total_TG,ApoA1,ApoB)%>%
tbl_summary(by = hba1c_categories,missing = "ifany",
statistic = list(all_continuous() ~ "{mean} ({sd})"))%>%
bold_labels() %>% add_overall() %>% add_p() %>%
modify_spanning_header(all_stat_cols() ~ "**Overall Sample**")%>%
modify_table_body(dplyr::mutate,
label = ifelse(label == "N missing (% missing)", "Unknown", label))%>%
as_flex_table() %>% align(align = "center",part = "all") %>%
autofit()
doc <- read_docx() %>% body_add_flextable(value = tab1, split = TRUE) %>%
body_end_section_landscape() %>%
print(target = "Proyectos/Prediabetes/tabla1.docx")
(86518*100/nrow(mcps_fin)) %>% round(1)
(23901*100/nrow(mcps_fin)) %>% round(1)
(8074*100/nrow(mcps_fin)) %>% round(1)
mcps_fin$pred_ADA %>% table %>% prop.table()
mcps_fin$pred_IEC %>% table %>% prop.table()
#### SuppTab1: Study pop by age----- ####
mcps_fin$age_decade <- cut(
x = mcps_fin$AGE, breaks = c(-Inf,45,55,65,75,Inf), right=F,
labels = c("35-44", "45-54", "55-64", "65-74", "≥75"))
setattr(mcps_fin$hba1c_categories, "label", "HbA1c category")
s_tab1 <- mcps_fin %>%
dplyr::select(age_decade,AGE,BASE_HBA1C,hba1c_categories,female,
COYOACAN,college,smoke,alcohol,phys,
BMI,ICE,icc,SBP1,DBP1,
Clinical_LDL_C,HDL_C,Total_TG,ApoA1,ApoB)%>%
tbl_summary(by = age_decade, missing = "ifany",
statistic = list(all_continuous() ~ "{mean} ({sd})"))%>%
bold_labels() %>% add_overall() %>% add_p() %>%
modify_spanning_header(all_stat_cols() ~ "**Overall Sample**")%>%
modify_table_body(dplyr::mutate, label = ifelse(
label == "N missing (% missing)", "Unknown", label))%>%
as_flex_table() %>% align(align = "center",part = "all") %>%
autofit()
doc <- read_docx() %>% body_add_flextable(value = s_tab1, split = TRUE) %>%
body_end_section_landscape() %>%
print(target = "Proyectos/Prediabetes/tabla_supp1.docx")
#### SuppMethods: ICD-10 codes------ ####
ICD10_ <- function(x){
(as.data.frame(table(x$ICD10_UNDERLYING))%>% `names<-`(c("Code", "Num")) %>%
transmute("A"=paste0(Code, " (", Num, ")")))$A %>% paste(collapse=", ")}
set_flextable_defaults(
font.size = 9, font.family = "Arial", table.layout="autofit", split=F,
table_align="center"); supp_methods <- data.frame(paste0(c(
"Cardiac", "Cerebrovascular", "Other vascular", "Renal", "Acute diabetic",
"Neoplastic", "Respiratory", "Gastrointestinal (including hepatobiliary)",
"External, ill-defined, or others"), " deaths, N=", mcps_fin %>% summarise(
sum(D003),sum(D008),sum(D014),sum(D019),sum(D023),sum(D040),sum(D044),
sum(D022+D045),sum(ext_ill)) %>% `names<-`(NULL)),
c(ICD10_(filter(mcps_fin, D003==1)), ICD10_(filter(mcps_fin, D008==1)),
ICD10_(filter(mcps_fin, D014==1)), ICD10_(filter(mcps_fin, D019==1)),
ICD10_(filter(mcps_fin, D023==1)), ICD10_(filter(mcps_fin, (D022+D045)==1)),
ICD10_(filter(mcps_fin, D040==1)), ICD10_(filter(mcps_fin, D044==1)),
ICD10_(filter(mcps_fin, ext_ill==1)))) %>% `colnames<-`(c(
"Cause-specific mortality, Number of deaths", "ICD-10 codes (N)")) %>%
flextable() %>% align(align="center", part="all") %>%
align(align="justify", j=2) %>% italic(part="all") %>%
bold(part="header") %>% autofit()
read_docx() %>% body_add_flextable(value=supp_methods) %>%
print(target = "Proyectos/Prediabetes/SuppMethods.docx")
#### Figure 1: Prediabetes prevalence---- ####
mcps_fin %>% filter(!is.na(BASE_HBA1C)) %>% nrow
#ADA
prev.all.ADA_male <- round(mean(mcps_fin$pred_ADA[mcps_fin$MALE==1],na.rm=T)*100,1)
prev.all.ADA_female <- round(mean(mcps_fin$pred_ADA[mcps_fin$MALE==0],na.rm=T)*100,1)
prev_ADA<- mcps_fin %>% mutate(category=cut(
AGE, breaks=c(-Inf, 40, 50, 55, 60, 65, 70, 75, Inf),
labels=c("35-39","40-49","50-54", "55-59", "60-64", "65-69", "70-74", "75-84"))) %>%
group_by(category, MALE) %>% summarise(n=n(), predm=sum(predm, na.rm = T)) %>%
mutate(prev=(predm/n)*100) %>% mutate(Sex=factor(MALE, labels=c("Female", "Male")))
temp<-NULL;L<-NULL;S<-NULL; for(i in 1:nrow(prev_ADA)) {
temp[[i]]<-exactci(prev_ADA$predm[[i]],prev_ADA$n[[i]],conf.level=0.95)
L[i]<-temp[[i]]$conf.int[1]; S[i]<-temp[[i]]$conf.int[2]}
prev_ADA$lower<-L*100; prev_ADA$upper<-S*100
prevalence_ADA<-prev_ADA %>%
ggplot(aes(y=prev, x=category, col=Sex, group=Sex))+
geom_point(size=2)+ geom_line(size=1)+
geom_errorbar(aes(ymin=lower, ymax=upper), width=.2)+
labs(x="Age groups", y="Prevalence (%)")+
scale_color_manual(values = c("#3B9AB2", "#EBCC2A"))+
theme_bw() + ggtitle("ADA-defined prediabetes") +
scale_y_continuous(limits = c(0,50.1))+
geom_hline(yintercept = prev.all.ADA_female, linetype="dashed", color=c("#3B9AB2"))+
geom_hline(yintercept = prev.all.ADA_male, linetype="dashed", color=c("#EBCC2A"))+
theme(text=element_text(size=15),
plot.title = element_text(face = "bold", hjust=0.5, size=15.5));prevalence_ADA
#IEC
prev.all.IEC_male <- round(mean(mcps_fin$predm_ice[mcps_fin$MALE==1],na.rm=T)*100,1)
prev.all.IEC_female <- round(mean(mcps_fin$predm_ice[mcps_fin$MALE==0],na.rm=T)*100,1)
prev_iec<- mcps_fin %>% mutate(category=cut(
AGE, breaks=c(-Inf, 40, 45, 50, 55, 60, 65, 70, 75,Inf),
labels=c("35-39","40-44","45-49","50-54", "55-59", "60-64", "65-69", "70-74", "75-84"))) %>%
group_by(category, MALE) %>% summarise(n=n(), predm=sum(predm_ice, na.rm = T)) %>%
mutate(prev=(predm/n)*100) %>% mutate(Sex=factor(MALE, labels=c("Female", "Male")))
temp<-NULL;L<-NULL;S<-NULL; for(i in 1:nrow(prev_iec)) {
temp[[i]]<-exactci(prev_iec$predm[[i]],prev_iec$n[[i]],conf.level=0.95)
L[i]<-temp[[i]]$conf.int[1]; S[i]<-temp[[i]]$conf.int[2]}
prev_iec$lower<-L*100; prev_iec$upper<-S*100
prevalence_iec<-prev_iec%>%
ggplot(aes(y=prev, x=category, col=Sex, group=Sex))+
geom_point(size=2)+ geom_line(size=1)+
geom_errorbar(aes(ymin=lower, ymax=upper), width=.2)+
labs(x="Age groups", y="Prevalence (%)")+
scale_color_manual(values = c("#3B9AB2", "#EBCC2A"))+
theme_bw() + ggtitle("IEC-defined prediabetes") +
scale_y_continuous(limits = c(0,50.1))+
geom_hline(yintercept = prev.all.IEC_female, linetype="dashed", color=c("#3B9AB2"))+
geom_hline(yintercept = prev.all.IEC_male, linetype="dashed", color=c("#EBCC2A"))+
theme(text=element_text(size=15),
plot.title = element_text(face = "bold", hjust=0.5, size=15.5));prevalence_iec
fig1<-ggarrange(prevalence_ADA, prevalence_iec, labels = c("A", "B"),
font.label = list(size = 16), common.legend = T, legend = "bottom")
ggsave(fig1, file="Proyectos/Prediabetes/Figure1.tif", bg="transparent",
width=25*1.3, height=15*1.3, units=c("cm"), dpi=600, limitsize = FALSE)
#### SuppFig2: Prediabetes risk by sex--- ####
# Lexis expansion
remove(mcps_lexis, mcps_fin2)
mcps_lexis <- Lexis(entry = list("period" = date_recruited, "age" = AGE, "time_at_entry" = 0),
exit = list("period" = date_recruited + PERSON_YEARS), exit.status = D000, data = mcps_fin)
mcps_fin2<-splitLexis(mcps_lexis, breaks=c(35,40,45,50,55,60,65,70,75,80,85), time.scale = "age")
mcps_fin2$ageout<-mcps_fin2$age+mcps_fin2$lex.dur; mcps_fin2$age.cat <- timeBand(mcps_fin2, "age", type = "factor")
mcps_fin2$premature<-ifelse(mcps_fin2$lex.Xst==T,1,0); mcps_fin2$premature[mcps_fin2$premature==1 & mcps_fin2$ageout>=75]<-0 #Deaths 35-74
mcps_fin2$old.death<-ifelse(mcps_fin2$lex.Xst==T,1,0); mcps_fin2$old.death[mcps_fin2$old.death==1 & mcps_fin2$ageout<75]<-0 #Deaths ≥75
mcps_fin2<- mcps_fin2 %>% rename(
"id" = lex.id, "fu_period" = lex.dur, "entry_status" = lex.Cst, "exit_status" = lex.Xst) %>%
mutate("time_at_exit" = time_at_entry + fu_period, "age_at_exit" = age + fu_period)
with(mcps_fin2, list(exit_status, old.death, premature)) %>% sapply(table)
#Deaths at 35-74 years old
m0.4A_female <- coxph(Surv(time_at_entry, time_at_exit, premature)~hba1c_cat+EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+factor(ALCGP)+COYOACAN+strata(age.cat)+cluster(id), data=mcps_fin2 %>% filter(MALE==0))
m0.4A_female_float <- float(object = m0.4A_female); dfAf <- data.frame(
"A1c"=(m0.4A_female_float$coef%>%names), "mean"=m0.4A_female_float$coef,
"Lower"=m0.4A_female_float$coef-qnorm(.975)*(m0.4A_female_float$var %>% sqrt), "Upper"=m0.4A_female_float$coef+qnorm(.975)*(m0.4A_female_float$var %>% sqrt)) %>%
mutate("HR"=exp(mean), "Lower"=exp(Lower), "Upper"=exp(Upper), "A1c"=ordered(A1c,levels=c("<5.7%","≥5.7% to <6.0%","≥6.0% to <6.5%")), "psize"=1/(mean+1)) %>%
`rownames<-`(NULL) %>% arrange(A1c) %>% mutate("N"=labels_ev3, "HR2"=sprintf("%#.2f",HR), "Sex"="Females")
m0.4A_male <- coxph(Surv(time_at_entry, time_at_exit, premature)~hba1c_cat+EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+factor(ALCGP)+COYOACAN+strata(age.cat)+cluster(id), data=mcps_fin2 %>% filter(MALE==1))
m0.4A_male_float <- float(object = m0.4A_male); dfAm <- data.frame(
"A1c"=(m0.4A_male_float$coef%>%names), "mean"=m0.4A_male_float$coef,
"Lower"=m0.4A_male_float$coef-qnorm(.975)*(m0.4A_male_float$var %>% sqrt), "Upper"=m0.4A_male_float$coef+qnorm(.975)*(m0.4A_male_float$var %>% sqrt)) %>%
mutate("HR"=exp(mean), "Lower"=exp(Lower), "Upper"=exp(Upper), "A1c"=ordered(A1c,levels=c("<5.7%","5.7-5.9%","6.0-6.4%")), "psize"=1/(mean+1)) %>%
`rownames<-`(NULL) %>% arrange(A1c) %>% mutate("N"=labels_ev4, "HR2"=sprintf("%#.2f",HR), "Sex"="Males")
rbind(dfAf, dfAm) %>%
ggplot(aes(x=A1c,color=A1c,y=HR,ymin=Lower,ymax=Upper,size=psize)) + geom_hline(yintercept = 1, linetype=2) +
geom_pointrange(shape=15) + facet_wrap(~Age, nrow=1, ncol=3) + coord_cartesian(ylim=c((0.85),(1.75))) +
scale_color_manual(values = wes_palette("Zissou1", 3)) + theme_bw() + scale_size_continuous(guide="none", range = c(0.7,1.3)) +
labs(color="HbA1c", y="Mortality Rate Ratio (95%CI)", x="\nHbA1c category", title=NULL) +
scale_y_continuous(trans = scales::log_trans()) +facet_wrap(~Sex)+
ggtitle("Deaths ocurring at ages 35-74 years")+
theme(legend.position="top", text=element_text(size=15), axis.text.x=element_blank(), axis.ticks.x = element_blank(),
strip.background = element_blank(), strip.text.x = element_text(face = "italic", size=11),
panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", hjust=0.5, size=13.5)) +
geom_text(aes(x=A1c,y=HR,label=HR2), color="black", nudge_x=0.2, nudge_y=0.05, size=3, fontface="bold.italic") +
geom_text(aes(x=A1c,y=Lower,label=N), color="black", nudge_x=0, nudge_y=-0.025, size=2.75) -> fs2b.f; fs2b.f
#Deaths at ≥75 years old
m0.4A_female <- coxph(Surv(time_at_entry, time_at_exit, old.death)~hba1c_cat+EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+factor(ALCGP)+COYOACAN+strata(age.cat)+cluster(id), data=mcps_fin2 %>% filter(MALE==0))
m0.4A_female_float <- float(object = m0.4A_female); dfAf_o <- data.frame(
"A1c"=(m0.4A_female_float$coef%>%names), "mean"=m0.4A_female_float$coef,
"Lower"=m0.4A_female_float$coef-qnorm(.975)*(m0.4A_female_float$var %>% sqrt), "Upper"=m0.4A_female_float$coef+qnorm(.975)*(m0.4A_female_float$var %>% sqrt)) %>%
mutate("HR"=exp(mean), "Lower"=exp(Lower), "Upper"=exp(Upper), "A1c"=ordered(A1c,levels=c("<5.7%","5.7-5.9%","6.0-6.4%")), "psize"=1/(mean+1)) %>%
`rownames<-`(NULL) %>% arrange(A1c) %>% mutate("N"=labels_ev5, "HR2"=sprintf("%#.2f",HR), "Sex"="Females")
m0.4A_male <- coxph(Surv(time_at_entry, time_at_exit, old.death)~hba1c_cat+EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+factor(ALCGP)+COYOACAN+strata(age.cat)+cluster(id), data=mcps_fin2 %>% filter(MALE==1))
m0.4A_male_float <- float(object = m0.4A_male); dfAm_o <- data.frame(
"A1c"=(m0.4A_male_float$coef%>%names), "mean"=m0.4A_male_float$coef,
"Lower"=m0.4A_male_float$coef-qnorm(.975)*(m0.4A_male_float$var %>% sqrt), "Upper"=m0.4A_male_float$coef+qnorm(.975)*(m0.4A_male_float$var %>% sqrt)) %>%
mutate("HR"=exp(mean), "Lower"=exp(Lower), "Upper"=exp(Upper), "A1c"=ordered(A1c,levels=c("<5.7%","5.7-5.9%","6.0-6.4%")), "psize"=1/(mean+1)) %>%
`rownames<-`(NULL) %>% arrange(A1c) %>% mutate("N"=labels_ev6, "HR2"=sprintf("%#.2f",HR), "Sex"="Males")
rbind(dfAf_o, dfAm_o) %>%
ggplot(aes(x=A1c,color=A1c,y=HR,ymin=Lower,ymax=Upper,size=psize)) + geom_hline(yintercept = 1, linetype=2) +
geom_pointrange(shape=15) + facet_wrap(~Age, nrow=1, ncol=3) + coord_cartesian(ylim=c((0.85),(1.75))) +
scale_color_manual(values = wes_palette("Zissou1", 3)) + theme_bw() + scale_size_continuous(guide="none", range = c(0.7,1.3)) +
labs(color="HbA1c", y="Mortality Rate Ratio (95%CI)", x="\nHbA1c category", title=NULL) +
scale_y_continuous(trans = scales::log_trans()) +facet_wrap(~Sex)+
ggtitle("Deaths ocurring at ages 75-84 years")+
theme(legend.position="top", text=element_text(size=15), axis.text.x=element_blank(), axis.ticks.x = element_blank(),
strip.background = element_blank(), strip.text.x = element_text(face = "italic", size=11),
panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", hjust=0.5, size=13.5)) +
geom_text(aes(x=A1c,y=HR,label=HR2), color="black", nudge_x=0.2, nudge_y=0.05, size=3, fontface="bold.italic") +
geom_text(aes(x=A1c,y=Lower,label=N), color="black", nudge_x=0, nudge_y=-0.025, size=2.75) -> fs2b.f_o; fs2b.f_o
#### Build figure 2
figs2a <- ggarrange(fs2b.f,fs2b.f_o, nrow=2, ncol=1, common.legend = T,
labels=c("A","B"), font.label = list(size = 17))
ggsave(figs2a, file="Proyectos/Prediabetes/FSupp2.tiff", bg="white", width=15*1.3, height=15*1.5, units=c("cm"), dpi=600, limitsize = FALSE)
remove(mcps_lexis, mcps_fin2)
#### Figure 2: (A) Death rates per cause- ####
mcps_fin$age_group <- mcps_fin$AGE %>%
cut(c(-Inf, 45,55,65,75,85, Inf), labels = 1:6, right = F)
table(mcps_fin$age_group, mcps_fin$D000)
table(mcps_fin$age_group, mcps_fin$D000_o)
table(mcps_fin$age_group, mcps_fin$D000_p)
get.death.rate <- function(x){
a <- x %>% summarise(cases=n(), time=sum(PERSON_YEARS))
time <- sum(a$time); cases <- a$cases[2]
lambda<-(cases/time)*1000; lambda.se<-(sqrt(cases/time^2))*1000
LI<-lambda-(qnorm(.975)*lambda.se); LS<-lambda+(qnorm(.975)*lambda.se)
cases.L<-LI*time/1000; cases.U<-LS*time/1000
data.frame(lambda, cases, cases.L, cases.U, time)} #Crude death rates
un.ag.st <- function(x){
a <- data.frame(); for(i in 1:length(table(mcps_fin$age_group))){
b <- x %>% filter(age_group==i) %>% get.death.rate()
a <- rbind(a, b)}; c <- a %>% cbind(
"age"=mcps_fin$age_group %>% levels); c} #Uniformly age and sex standardized
un.ag.st.CI <- function(x){
x %>% na.omit %>% mutate("w"=1/length(age)) %>%
mutate("var"=(((w)^2*lambda)/time)) %>% summarise(
lambda=mean(lambda), var=sum(var), lambda.se=sqrt(sum(var)),
cases=sum(cases), cases.L=sum(cases.L), cases.U=sum(cases.U)) %>%
mutate("Lower"=lambda+(cases.L-cases)*sqrt(var/cases),
"Upper"=lambda+(cases.U-cases)*sqrt(var/cases))} #Standardized confidence intervals
#------------------ PREMATURE DEATHS ------------------#
## All-cause mortality ##
#HBA1C <5.7
rates1A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D000_p) %>% un.ag.st
rates1A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D000_p) %>% un.ag.st
rates1A <- rbind(rates1A.M, rates1A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates1B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D000_p) %>% un.ag.st
rates1B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D000_p) %>% un.ag.st
rates1B <- rbind(rates1B.M, rates1B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates1C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D000_p) %>% un.ag.st
rates1C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D000_p) %>% un.ag.st
rates1C <- rbind(rates1C.M, rates1C.F) %>% un.ag.st.CI
rates1 <- rbind(rates1A,rates1B,rates1C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Overall")
## Cardiac ## D003
#HBA1C <5.7
rates2A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D003_p) %>% un.ag.st
rates2A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D003_p) %>% un.ag.st
rates2A <- rbind(rates2A.M, rates2A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates2B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D003_p) %>% un.ag.st
rates2B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D003_p) %>% un.ag.st
rates2B <- rbind(rates2B.M, rates2B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates2C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D003_p) %>% un.ag.st
rates2C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D003_p) %>% un.ag.st
rates2C <- rbind(rates2C.M, rates2C.F) %>% un.ag.st.CI
rates2 <- rbind(rates2A,rates2B,rates2C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Cardiac")
## Cerebrovascular ## D008
#HBA1C <5.7
rates3A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D008_p) %>% un.ag.st
rates3A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D008_p) %>% un.ag.st
rates3A <- rbind(rates3A.M, rates3A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates3B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D008_p) %>% un.ag.st
rates3B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D008_p) %>% un.ag.st
rates3B <- rbind(rates3B.M, rates3B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates3C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D008_p) %>% un.ag.st
rates3C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D008_p) %>% un.ag.st
rates3C <- rbind(rates3C.M, rates3C.F) %>% un.ag.st.CI
rates3 <- rbind(rates3A,rates3B,rates3C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Cerebrovascular")
## Other vascular ## D014
#HBA1C <5.7
rates4A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D014_p) %>% un.ag.st
rates4A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D014_p) %>% un.ag.st
rates4A <- rbind(rates4A.M, rates4A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates4B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D014_p) %>% un.ag.st
rates4B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D014_p) %>% un.ag.st
rates4B <- rbind(rates4B.M, rates4B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates4C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D014_p) %>% un.ag.st
rates4C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D014_p) %>% un.ag.st
rates4C <- rbind(rates4C.M, rates4C.F) %>% un.ag.st.CI
rates4 <- rbind(rates4A,rates4B,rates4C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Other vascular")
## Renal ## D019
#HBA1C <5.7
rates5A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D019_p) %>% un.ag.st
rates5A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D019_p) %>% un.ag.st
rates5A <- rbind(rates5A.M, rates5A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates5B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D019_p) %>% un.ag.st
rates5B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D019_p) %>% un.ag.st
rates5B <- rbind(rates5B.M, rates5B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates5C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D019_p) %>% un.ag.st
rates5C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D019_p) %>% un.ag.st
rates5C <- rbind(rates5C.M, rates5C.F) %>% un.ag.st.CI
rates5 <- rbind(rates5A,rates5B,rates5C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Renal")
## Acute diabetic ## D023
#HBA1C <5.7
rates6A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D023_p) %>% un.ag.st
rates6A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D023_p) %>% un.ag.st
rates6A <- rbind(rates6A.M, rates6A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates6B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D023_p) %>% un.ag.st
rates6B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D023_p) %>% un.ag.st
rates6B <- rbind(rates6B.M, rates6B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates6C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D023_p) %>% un.ag.st
rates6C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D023_p) %>% un.ag.st
rates6C <- rbind(rates6C.M, rates6C.F) %>% un.ag.st.CI
rates6 <- rbind(rates6A,rates6B,rates6C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Acute diabetic")
## Other gastrointestinal ## D045
#HBA1C <5.7
rates8A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D045_p) %>% un.ag.st
rates8A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D045_p) %>% un.ag.st
rates8A <- rbind(rates8A.M, rates8A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates8B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D045_p) %>% un.ag.st
rates8B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D045_p) %>% un.ag.st
rates8B <- rbind(rates8B.M, rates8B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates8C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D045_p) %>% un.ag.st
rates8C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D045_p) %>% un.ag.st
rates8C <- rbind(rates8C.M, rates8C.F) %>% un.ag.st.CI
rates8 <- rbind(rates8A,rates8B,rates8C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Other\ngastrointestinal")
## Respiratory ## D044
#HBA1C <5.7
rates9A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D044_p) %>% un.ag.st
rates9A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D044_p) %>% un.ag.st
rates9A <- rbind(rates9A.M, rates9A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates9B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D044_p) %>% un.ag.st
rates9B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D044_p) %>% un.ag.st
rates9B <- rbind(rates9B.M, rates9B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates9C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D044_p) %>% un.ag.st
rates9C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D044_p) %>% un.ag.st
rates9C <- rbind(rates9C.M, rates9C.F) %>% un.ag.st.CI
rates9 <- rbind(rates9A,rates9B,rates9C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Respiratory")
## Neoplastic ## D040
#HBA1C <5.7
rates10A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D040_p) %>% un.ag.st
rates10A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D040_p) %>% un.ag.st
rates10A <- rbind(rates10A.M, rates10A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates10B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D040_p) %>% un.ag.st
rates10B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D040_p) %>% un.ag.st
rates10B <- rbind(rates10B.M, rates10B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates10C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D040_p) %>% un.ag.st
rates10C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D040_p) %>% un.ag.st
rates10C <- rbind(rates10C.M, rates10C.F) %>% un.ag.st.CI
rates10 <- rbind(rates10A,rates10B,rates10C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Neoplastic")
## Other ## ext_ill
#HBA1C <5.7
rates11A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(ext_ill_p) %>% un.ag.st
rates11A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(ext_ill_p) %>% un.ag.st
rates11A <- rbind(rates11A.M, rates11A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates11B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>%group_by(ext_ill_p) %>% un.ag.st
rates11B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(ext_ill_p) %>% un.ag.st
rates11B <- rbind(rates11B.M, rates11B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates11C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(ext_ill_p) %>% un.ag.st
rates11C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(ext_ill_p) %>% un.ag.st
rates11C <- rbind(rates11C.M, rates11C.F) %>% un.ag.st.CI
rates11 <- rbind(rates11A,rates11B,rates11C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Other")
rbind(rates1, rates2, rates3, rates5,
rates6, rates8, rates9, rates10) %>% arrange(desc(lambda)) %>%
mutate(pred=reorder(pred, -lambda, decreasing=T), cause=reorder(cause, -lambda, decreasing=T)) %>%
ggplot(aes(y=lambda, fill=pred, x=cause)) + geom_bar(stat="identity", position='dodge', color="black", width=0.8) +
theme_bw() + scale_fill_manual(values = wes_palette("Zissou1", 3)) +
labs(fill="HbA1c", y="Deaths per 1,000 person-years (uniformly age- and sex-standardized)") +
geom_errorbar(aes(ymin=Lower, ymax=Upper), width=.2, position=position_dodge(.8)) +
coord_flip() + scale_y_break(c(1.5,3), scales = 0.25, ticklabels = c(3,4)) + xlab(NULL) +
theme(legend.position="top", text=element_text(size=15), panel.grid.minor = element_blank(),
plot.title = element_text(face = "bold", hjust=0.5, size=13.5)) -> f2a.1
#------------------ DEATHS AT OLDER AGES ------------------#
## All-cause mortality ##
#HBA1C <5.7
rates1A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D000_o) %>% un.ag.st
rates1A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D000_o) %>% un.ag.st
rates1A <- rbind(rates1A.M, rates1A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates1B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D000_o) %>% un.ag.st
rates1B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D000_o) %>% un.ag.st
rates1B <- rbind(rates1B.M, rates1B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates1C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D000_o) %>% un.ag.st
rates1C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D000_o) %>% un.ag.st
rates1C <- rbind(rates1C.M, rates1C.F) %>% un.ag.st.CI
rates1o <- rbind(rates1A,rates1B,rates1C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Overall")
## Cardiac ## D003
#HBA1C <5.7
rates2A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D003_o) %>% un.ag.st
rates2A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D003_o) %>% un.ag.st
rates2A <- rbind(rates2A.M, rates2A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates2B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D003_o) %>% un.ag.st
rates2B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D003_o) %>% un.ag.st
rates2B <- rbind(rates2B.M, rates2B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates2C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D003_o) %>% un.ag.st
rates2C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D003_o) %>% un.ag.st
rates2C <- rbind(rates2C.M, rates2C.F) %>% un.ag.st.CI
rates2o <- rbind(rates2A,rates2B,rates2C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Cardiac")
## Cerebrovascular ## D008
#HBA1C <5.7
rates3A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D008_o) %>% un.ag.st
rates3A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D008_o) %>% un.ag.st
rates3A <- rbind(rates3A.M, rates3A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates3B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D008_o) %>% un.ag.st
rates3B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D008_o) %>% un.ag.st
rates3B <- rbind(rates3B.M, rates3B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates3C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D008_o) %>% un.ag.st
rates3C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D008_o) %>% un.ag.st
rates3C <- rbind(rates3C.M, rates3C.F) %>% un.ag.st.CI
rates3o <- rbind(rates3A,rates3B,rates3C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Cerebrovascular")
## Other vascular ## D014
#HBA1C <5.7
rates4A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D014_o) %>% un.ag.st
rates4A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D014_o) %>% un.ag.st
rates4A <- rbind(rates4A.M, rates4A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates4B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D014_o) %>% un.ag.st
rates4B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D014_o) %>% un.ag.st
rates4B <- rbind(rates4B.M, rates4B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates4C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D014_o) %>% un.ag.st
rates4C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D014_o) %>% un.ag.st
rates4C <- rbind(rates4C.M, rates4C.F) %>% un.ag.st.CI
rates4o <- rbind(rates4A,rates4B,rates4C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Other vascular")
## Renal ## D019
#HBA1C <5.7
rates5A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D019_o) %>% un.ag.st
rates5A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D019_o) %>% un.ag.st
rates5A <- rbind(rates5A.M, rates5A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates5B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D019_o) %>% un.ag.st
rates5B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D019_o) %>% un.ag.st
rates5B <- rbind(rates5B.M, rates5B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates5C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D019_o) %>% un.ag.st
rates5C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D019_o) %>% un.ag.st
rates5C <- rbind(rates5C.M, rates5C.F) %>% un.ag.st.CI
rates5o <- rbind(rates5A,rates5B,rates5C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Renal")
## Acute diabetic ## D023
#HBA1C <5.7
rates6A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D023_o) %>% un.ag.st
rates6A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D023_o) %>% un.ag.st
rates6A <- rbind(rates6A.M, rates6A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates6B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D023_o) %>% un.ag.st
rates6B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D023_o) %>% un.ag.st
rates6B <- rbind(rates6B.M, rates6B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates6C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D023_o) %>% un.ag.st
rates6C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D023_o) %>% un.ag.st
rates6C <- rbind(rates6C.M, rates6C.F) %>% un.ag.st.CI
rates6o <- rbind(rates6A,rates6B,rates6C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Acute diabetic")
## Other gastrointestinal ## D045
#HBA1C <5.7
rates8A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D045_o) %>% un.ag.st
rates8A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D045_o) %>% un.ag.st
rates8A <- rbind(rates8A.M, rates8A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates8B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D045_o) %>% un.ag.st
rates8B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D045_o) %>% un.ag.st
rates8B <- rbind(rates8B.M, rates8B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates8C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D045_o) %>% un.ag.st
rates8C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D045_o) %>% un.ag.st
rates8C <- rbind(rates8C.M, rates8C.F) %>% un.ag.st.CI
rates8o <- rbind(rates8A,rates8B,rates8C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Other\ngastrointestinal")
## Respiratory ## D044
#HBA1C <5.7
rates9A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D044_o) %>% un.ag.st
rates9A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D044_o) %>% un.ag.st
rates9A <- rbind(rates9A.M, rates9A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates9B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D044_o) %>% un.ag.st
rates9B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D044_o) %>% un.ag.st
rates9B <- rbind(rates9B.M, rates9B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates9C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D044_o) %>% un.ag.st
rates9C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D044_o) %>% un.ag.st
rates9C <- rbind(rates9C.M, rates9C.F) %>% un.ag.st.CI
rates9o <- rbind(rates9A,rates9B,rates9C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Respiratory")
## Neoplastic ## D040
#HBA1C <5.7
rates10A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(D040_o) %>% un.ag.st
rates10A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(D040_o) %>% un.ag.st
rates10A <- rbind(rates10A.M, rates10A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates10B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(D040_o) %>% un.ag.st
rates10B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(D040_o) %>% un.ag.st
rates10B <- rbind(rates10B.M, rates10B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates10C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(D040_o) %>% un.ag.st
rates10C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(D040_o) %>% un.ag.st
rates10C <- rbind(rates10C.M, rates10C.F) %>% un.ag.st.CI
rates10o <- rbind(rates10A,rates10B,rates10C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Neoplastic")
## Other ## ext_ill
#HBA1C <5.7
rates11A.M <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==1) %>% group_by(ext_ill_o) %>% un.ag.st
rates11A.F <- mcps_fin %>% filter(hba1c_cat=="<5.7%", MALE==0) %>% group_by(ext_ill_o) %>% un.ag.st
rates11A <- rbind(rates11A.M, rates11A.F) %>% un.ag.st.CI
#HBA1C 5.7-5.9
rates11B.M <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==1) %>% group_by(ext_ill_o) %>% un.ag.st
rates11B.F <- mcps_fin %>% filter(hba1c_cat=="5.7-5.9%", MALE==0) %>% group_by(ext_ill_o) %>% un.ag.st
rates11B <- rbind(rates11B.M, rates11B.F) %>% un.ag.st.CI
#HBA1C ≥6.0
rates11C.M <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==1) %>% group_by(ext_ill_o) %>% un.ag.st
rates11C.F <- mcps_fin %>% filter(hba1c_cat=="6.0-6.4%", MALE==0) %>% group_by(ext_ill_o) %>% un.ag.st
rates11C <- rbind(rates11C.M, rates11C.F) %>% un.ag.st.CI
rates11o <- rbind(rates11A,rates11B,rates11C) %>% as_tibble %>% mutate(
pred=c("<5.7%","≥5.7 to <6.0%","≥6.0 to <6.5%"), cause="Other")
rbind(rates1o, rates2o, rates3o, rates5o,
rates6o, rates8o, rates9o, rates10o) %>% arrange(desc(lambda)) %>%
mutate(pred=reorder(pred, -lambda, decreasing=T), cause=reorder(cause, -lambda, decreasing=T)) %>%
ggplot(aes(y=lambda, fill=pred, x=cause)) + geom_bar(stat="identity", position='dodge', color="black", width=0.8) +
theme_bw() + scale_fill_manual(values = wes_palette("Zissou1", 3)) +
labs(fill="HbA1c", y="Deaths per 1,000 person-years (uniformly age- and sex-standardized)") +
geom_errorbar(aes(ymin=Lower, ymax=Upper), width=.2, position=position_dodge(.8)) +
coord_flip() + scale_y_break(c(7,15), scales = 0.25, ticklabels = c(15,18)) + xlab(NULL) +
theme(legend.position="top", text=element_text(size=15), panel.grid.minor = element_blank(),
plot.title = element_text(face = "bold", hjust=0.5, size=13.5)) -> f2a.2
####
rbind(rates1, rates2, rates3, rates4, rates5, rates6, rates8, rates9, rates10, rates11) %>% group_by(cause) %>%
mutate(RR=lambda/lambda[pred=="<5.7%"], RR.se=sqrt((1/cases[pred=="<5.7%"])+(1/cases)),
RR.LL=RR-qnorm(.975)*RR.se,RR.UL=RR+qnorm(.975)*RR.se)
rbind(rates1o, rates2o, rates3o, rates4o, rates5o, rates6o, rates8o, rates9o, rates10o, rates11o) %>% group_by(cause) %>%
mutate(RR=lambda/lambda[pred=="<5.7%"], RR.se=sqrt((1/cases[pred=="<5.7%"])+(1/cases)),
RR.LL=RR-qnorm(.975)*RR.se,RR.UL=RR+qnorm(.975)*RR.se)
#### Figure 2: (B) Age*HbA1c (+SuppFig3)- ####
# Lexis expansion
remove(mcps_lexis, mcps_fin2)
mcps_lexis <- Lexis(entry = list("period" = date_recruited, "age" = AGE, "time_at_entry" = 0),
exit = list("period" = date_recruited + PERSON_YEARS), exit.status = D000, data = mcps_fin)
mcps_fin2<-splitLexis(mcps_lexis, breaks=c(35,40,45,50,55,60,65,70,75,80,85), time.scale = "age")
mcps_fin2$ageout<-mcps_fin2$age+mcps_fin2$lex.dur; mcps_fin2$age.cat <- timeBand(mcps_fin2, "age", type = "factor")
mcps_fin2$premature<-ifelse(mcps_fin2$lex.Xst==T,1,0); mcps_fin2$premature[mcps_fin2$premature==1 & mcps_fin2$ageout>=75]<-0 #Deaths 35-74
mcps_fin2$old.death<-ifelse(mcps_fin2$lex.Xst==T,1,0); mcps_fin2$old.death[mcps_fin2$old.death==1 & mcps_fin2$ageout<75]<-0 #Deaths ≥75
mcps_fin2<- mcps_fin2 %>% rename(
"id" = lex.id, "fu_period" = lex.dur, "entry_status" = lex.Cst, "exit_status" = lex.Xst) %>%
mutate("time_at_exit" = time_at_entry + fu_period, "age_at_exit" = age + fu_period)
with(mcps_fin2, list(exit_status, old.death, premature)) %>% sapply(table)
# Baseline a1c and age categories
mcps_fin2 <- mcps_fin2 %>% mutate(
"a1c_int"=cut(BASE_HBA1C, c(-Inf, 5.7, 6, Inf), right=F) %>% factor(labels=c("<5.7%","5.7-5.9%","6.0-6.4%")),
"age_int"=cut(AGE, c(-Inf, 50, 64, Inf), right=F) %>% factor(labels=c("35-50","50-64","≥65")),
"age_int2"=cut(AGE, c(-Inf, 65, 69, Inf), right=F) %>% factor(labels=c("<65","65-69","≥70")),
"a1c_age"=paste0(a1c_int,", ",age_int) %>% factor(levels=c("<5.7%, 35-50","<5.7%, 50-64","<5.7%, ≥65",
"5.7-5.9%, 35-50","5.7-5.9%, 50-64","5.7-5.9%, ≥65",
"6.0-6.4%, 35-50","6.0-6.4%, 50-64","6.0-6.4%, ≥65")),
"a1c_age2"=paste0(a1c_int,", ",age_int2) %>% factor(levels=c("<5.7%, <65","<5.7%, 65-69","<5.7%, ≥70",
"5.7-5.9%, <65","5.7-5.9%, 65-69","5.7-5.9%, ≥70",
"6.0-6.4%, <65","6.0-6.4%, 65-69","6.0-6.4%, ≥70")))
#Deaths at 35-74 years old
(mcps_fin2 %>% filter(between(AGE,35,74)) %>% transmute(
"Out"=premature, "A1c"=a1c_age, MALE, EDU_LEVEL) %>% na.omit %>%
select(Out,A1c) %>% table)[2,] -> labels_ev1
m0.4A <- coxph(Surv(time_at_entry, time_at_exit, premature)~a1c_age+EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+factor(ALCGP)+COYOACAN+strata(MALE)+strata(age.cat)+cluster(id), data=mcps_fin2)
m0.4A_float <- float(object = m0.4A); dfA <- data.frame(
"A1c"=(m0.4A_float$coef%>%names) %>% str_extract("([<≥]?\\d)([.])(\\d)([-]\\d[.]\\d)?([%])"),
"Age"=(m0.4A_float$coef%>%names) %>% str_extract("([<≥]?\\d{2})([-]\\d{2})?"), "mean"=m0.4A_float$coef,
"Lower"=m0.4A_float$coef-qnorm(.975)*(m0.4A_float$var %>% sqrt), "Upper"=m0.4A_float$coef+qnorm(.975)*(m0.4A_float$var %>% sqrt)) %>%
mutate("Age"=ordered(Age,levels=c("35-50","50-64","≥65"), labels=c("Baseline age <50 years","≥50 to <65 years","≥65 years"))) %>%
mutate("HR"=exp(mean), "Lower"=exp(Lower), "Upper"=exp(Upper), "A1c"=ordered(A1c,levels=c("<5.7%","5.7-5.9%","6.0-6.4%"), labels=c("<5.7%","≥5.7% to <6.0%","≥6.0% to <6.5%")), "psize"=1/(mean+1)) %>%
`rownames<-`(NULL) %>% arrange(A1c,Age) %>% mutate("N"=labels_ev1, "HR2"=sprintf("%#.2f",HR)); dfA %>%
ggplot(aes(x=A1c,color=A1c,y=HR,ymin=Lower,ymax=Upper,size=psize)) + geom_hline(yintercept = 1, linetype=2) +
geom_pointrange(shape=15) + facet_wrap(~Age, nrow=1, ncol=3) + coord_cartesian(ylim=c((0.85),(2.15))) +
scale_color_manual(values = wes_palette("Zissou1", 3)) + theme_bw() + scale_size_continuous(guide="none", range = c(0.7,1.3)) +
labs(color="HbA1c", y="Mortality Rate Ratio (95%CI)", x="\nAge-HbA1c category", title=NULL) +
scale_y_continuous(trans = scales::log_trans()) +
theme(legend.position="top", text=element_text(size=15), axis.text.x=element_blank(), axis.ticks.x = element_blank(),
strip.background = element_blank(), strip.text.x = element_text(face = "italic", size=11),
panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", hjust=0.5, size=13.5)) +
geom_text(aes(x=A1c,y=HR,label=HR2), color="black", nudge_x=0.2, nudge_y=0.05, size=3, fontface="bold.italic") +
geom_text(aes(x=A1c,y=Lower,label=N), color="black", nudge_x=0, nudge_y=-0.025, size=2.75) -> f2b.1; f2b.1
#Deaths at ≥75 years old
(mcps_fin2 %>% filter(age_risk>=75) %>% transmute(
"Out"=old.death, "A1c"=a1c_age2, MALE, EDU_LEVEL) %>% na.omit %>%
select(Out,A1c) %>% table)[2,] -> labels_ev2
m0.4B <- coxph(Surv(time_at_entry, time_at_exit, old.death)~a1c_age2+EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+factor(ALCGP)+COYOACAN+strata(MALE)+strata(age.cat)+cluster(id), data=mcps_fin2)
m0.4B_float <- float(object = m0.4B); dfB <- data.frame(
"A1c"=(m0.4B_float$coef%>%names) %>% str_extract("([<≥]?\\d)([.])(\\d)([-]\\d[.]\\d)?([%])"),
"Age"=(m0.4B_float$coef%>%names) %>% str_extract("([<≥]?\\d{2})([-]\\d{2})?"), "mean"=m0.4B_float$coef,
"Lower"=m0.4B_float$coef-qnorm(.975)*(m0.4B_float$var %>% sqrt), "Upper"=m0.4B_float$coef+qnorm(.975)*(m0.4B_float$var %>% sqrt)) %>%
mutate("Age"=ordered(Age,levels=c("<65","65-69","≥70"), labels=c("Baseline age <65 years","≥65 to <70 years","≥70 years"))) %>%
mutate("HR"=exp(mean), "Lower"=exp(Lower), "Upper"=exp(Upper), "A1c"=ordered(A1c,levels=c("<5.7%","5.7-5.9%","6.0-6.4%"), labels=c("<5.7%","≥5.7% to <6.0%","≥6.0% to <6.5%")), "psize"=1/(mean+1)) %>%
`rownames<-`(NULL) %>% arrange(A1c,Age) %>% mutate("N"=labels_ev2, "HR2"=sprintf("%.2f",HR)); dfB %>%
ggplot(aes(x=A1c,color=A1c,y=HR,ymin=Lower,ymax=Upper,size=psize)) + geom_hline(yintercept = 1, linetype=2) +
geom_pointrange(shape=15) + facet_wrap(~Age, nrow=1, ncol=3) + coord_cartesian(ylim=c(0.85,2.15)) +
scale_color_manual(values = wes_palette("Zissou1", 3)) + theme_bw() + scale_size_continuous(guide="none", range = c(0.7,1.3))+
labs(color="HbA1c", y="Mortality Rate Ratio (95%CI)", x="\nAge-HbA1c category", title=NULL) +
scale_y_continuous(trans = scales::log_trans()) +
theme(legend.position="top", text=element_text(size=15), axis.text.x=element_blank(), axis.ticks.x = element_blank(),
strip.background = element_blank(), strip.text.x = element_text(face = "italic", size=11),
panel.grid.minor = element_blank(), plot.title = element_text(face = "bold", hjust=0.5, size=13.5)) +
geom_text(aes(x=A1c,y=HR,label=HR2), color="black", nudge_x=0.2, nudge_y=0.05, size=3, fontface="bold.italic") +
geom_text(aes(x=A1c,y=Lower,label=N), color="black", nudge_x=0, nudge_y=-0.025, size=2.75) -> f2b.2; f2b.2
#### Build figure 2
fig2 <- ggarrange(print(f2a.1), f2b.1, nrow=1, ncol=2, labels=c("A","B"), font.label = list(size = 17))
ggsave(fig2, file="Proyectos/Prediabetes/Figure2.tif", bg="white", width=30*1.3, height=15*1.5, units=c("cm"), dpi=600, limitsize = FALSE)
#### Build figure 2
supp2 <- ggarrange(print(f2a.2), f2b.2, nrow=1, ncol=2, labels=c("A","B"), font.label = list(size = 17)) %>%
annotate_figure(top=text_grob("Deaths ocurring at ages 75-84 years", face="bold.italic", size=18, hjust=0.5))
ggsave(supp2, file="Proyectos/Prediabetes/FSupp3.png", bg="white", width=30*1.3, height=15*1.5, units=c("cm"), dpi=600, limitsize = FALSE)
#### RevDCare: A1c*Age*Sex + splines----- ####
### TEST TRIPLE INTERACTION
# Lexis expansion
remove(mcps_lexis, mcps_fin2)
mcps_lexis <- Lexis(entry = list("period" = date_recruited, "age" = AGE, "time_at_entry" = 0),
exit = list("period" = date_recruited + PERSON_YEARS), exit.status = D000, data = mcps_fin)
mcps_fin2<-splitLexis(mcps_lexis, breaks=c(35,40,45,50,55,60,65,70,75,80,85), time.scale = "age")
mcps_fin2$ageout<-mcps_fin2$age+mcps_fin2$lex.dur; mcps_fin2$age.cat <- timeBand(mcps_fin2, "age", type = "factor")
mcps_fin2$premature<-ifelse(mcps_fin2$lex.Xst==T,1,0); mcps_fin2$premature[mcps_fin2$premature==1 & mcps_fin2$ageout>=75]<-0 #Deaths 35-74
mcps_fin2$old.death<-ifelse(mcps_fin2$lex.Xst==T,1,0); mcps_fin2$old.death[mcps_fin2$old.death==1 & mcps_fin2$ageout<75]<-0 #Deaths ≥75
mcps_fin2<- mcps_fin2 %>% rename(
"id" = lex.id, "fu_period" = lex.dur, "entry_status" = lex.Cst, "exit_status" = lex.Xst) %>%
mutate("time_at_exit" = time_at_entry + fu_period, "age_at_exit" = age + fu_period)
with(mcps_fin2, list(exit_status, old.death, premature)) %>% sapply(table)
#DC revision: Quantify HbA1c*Age and HbA1c*Sex interactions
#Premature deaths
coxph( #HbA1c*Sex
Surv(time_at_entry,time_at_exit,premature)~BASE_HBA1C*MALE+
EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+ factor(ALCGP)+COYOACAN+
strata(age.cat), data=mcps_fin2) -> test.int_1
coxph( #HbA1c*AGE
Surv(time_at_entry,time_at_exit,premature)~BASE_HBA1C*AGE+
EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+ factor(ALCGP)+COYOACAN+
strata(age.cat)+strata(MALE), data=mcps_fin2) -> test.int_2
#Older deaths
coxph( #HbA1c*Sex
Surv(time_at_entry,time_at_exit,old.death)~BASE_HBA1C*MALE+
EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+ factor(ALCGP)+COYOACAN+
strata(age.cat), data=mcps_fin2) -> test.int_4
coxph( #HbA1c*AGE
Surv(time_at_entry,time_at_exit,old.death)~BASE_HBA1C*AGE+
EDU_LEVEL+factor(PHYSGP)+factor(SMOKEGP)+ factor(ALCGP)+COYOACAN+
strata(age.cat)+strata(MALE), data=mcps_fin2) -> test.int_5
#Premature deaths