## 定义本代码基于 **NHANES 1999–2023** 年数据，纳入年龄 **≥20 岁**、拥有 **空腹样本权重** 的成年人群。与 CKM（一）不同，本代码**不采用种族限制**，即 BMI 和腰围的阈值**不分亚裔与非亚裔**，所有种族均采用统一标准。这一处理方式可扩大样本覆盖范围，避免因亚裔种族分类信息导致的样本缺失，使 CKM 分期结果更具人群普适性。

Definition

Criteria

detail

Cardiovascular-kidney-metabolic (CKM)

1、CKM Stage 0
All of the following conditions are met：
• normal BMI： < 25 kg/m²
• normal waist circumference：women < 88 cm, men < 102 cm
• did not meet criteria for other stages

2、CKM Stage 1
Meet any of the following conditions:
• elevated BMI：≥ 25 kg/m²
• elevated waist circumference：women ≥ 88 cm, men ≥ 102 cm
• prediabetes ： HbA1c 5.7% to < 6.5% or fasting glucose 100 to < 126 mg/dL

3、CKM Stage 2
Meet any of the following conditions:
• metabolic risk factors：elevated fasting triglycerides ≥ 135 mg/dL, hypertension, diabetes, or metabolic syndrome defined as ≥ 3 of: elevated waist circumference, low HDL-C [< 40 mg/dL men, < 50 mg/dL women], fasting serum triglycerides ≥ 150 mg/dL, elevated BP [SBP ≥130 or DBP ≥80 mmHg or use of blood pressure-lowering medications] or prediabetes.
• moderate-to-high-risk CKD per Kidney Disease Improving Global Outcomes (KDIGO) criteria

4、CKM Stage 3
Meet any of the following conditions:
• very-high-risk KDIGO CKD stages
• high predicted 10-year CVD risk： ≥ 20% by AHA PREVENT equations

5、CKM Stage 4
Participants with self-reported established CVD including coronary heart disease, angina, heart attack, heart failure, or stroke.

CKM_stage

←

WTSAF4YR, WTSAFPRP, WTSAF2YR, MCQ160C, MCQ160B, MCQ160E, MCQ160F, MCQ160D, LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR, URXUMA, URXUCR, LBXTC, LBDHDL, LBXHDD, LBDHDD, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU, SMQ040, SMQ020, BPQ050A, BPQ150, LBXTR, LBXTLG, BPQ020, BPQ030, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, BMXWAIST, BMXBMI

类别分布:

14945	52.2%	2
5735	20%	1
3264	11.4%	4
2368	8.3%	0
2309	8.1%	3

> Aggarwal R, Ostrominski JW, Vaduganathan M. Prevalence of Cardiovascular-Kidney-Metabolic Syndrome Stages in US Adults, 2011-2020. JAMA. 2024 Jun 4;331(21):1858-1860. doi: 10.1001/jama.2024.6892. PMID: 38717747; PMCID: PMC11079779. ## 材料 ### 1.1-提取变量

MCQ160B=CHF, MCQ160D=angina, MCQ160C=CHD, MCQ160E=heart_attack, MCQ160F=stroke, RIAGENDR=sex, RIDAGEYR=age, WTSAFPRP, WTSAF2YR, WTSAF4YR, LBXSCR=cr_mg.dl_1, LBDSCR=cr_mg.dl_2, DIQ010=DM_told, DIQ070=DM_pills_now_1, DID070=DM_pills_now_2, DIQ050=DM_insulin_now, LBXGLU=fpg_mg.dl, LBXGH=HbA1c, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, BPQ020=HBP_told, BPQ040A=HBP_told_treat, BPQ050A=HBP_treat_1, BPQ150=HBP_treat_2, BPQ030=HBP_told2time, BMXBMI=BMI, BMXWAIST=waist_cir, LBXTR=ftg_mg.dl_1, LBXTLG=ftg_mg.dl_2, LBDHDL=HDLC_mg.dl_1, LBXHDD=HDLC_mg.dl_2, LBDHDD=HDLC_mg.dl_3, URXUMA=ualb_ug.ml, URXUCR=ucrea_mg.dl, LBXTC=TC_mg.dl, SMQ020, SMQ040

### 1.2-提取处方数据

治疗分类	命名	输入字段	方式	操作
他汀	statin

### 2-代码材料 ``` rm(list = ls()) # 设置路径 # ----------- 检查并安装包 ----------- for (pkg in c("devtools", "openxlsx", "dplyr")) { if (!requireNamespace(pkg, quietly = TRUE)) { install.packages(pkg) } } library("devtools") library("openxlsx") library("dplyr") install_github("ZhidoHuang/dbCodeBookr") # 提示更新包, 选 3 回车跳过，首次使用时需要安装 library("dbCodeBookr") # ----------- 读取数据 ----------- dt <- read.csv("raw_data.csv") # 主数据（主要变量） dt1<-read.csv("raw_drug.csv") # 药物信息 dt <- left_join(dt, dt1, by = "SEQN") # 将主数据与药物信息合并 data <-dt # 重要！！！ data[data==""] <- NA # 重要！！！ names(data) # ----------- 批量合并数据 ----------- # 批量合并后缀_0, _1 data<-yyds_coalesce(data, key_chr = c("_1", "_2","_3"), # 后缀标记(要合并的) pattern = c("ends_with"), # "ends_with", "starts_with", "contains" new_suffix = "", # 新后缀 valid_var = "YEAR", # 检查 remove_original = T) # 是否删除原始变量 names(data) # ----------- CKM 第 4 阶段 ----------- ## 清理"CHD", "CHF", "stroke", "angina", "heart_attack" #### # 查看数据类型 sapply(data[,c("CHD","CHF","stroke","angina","heart_attack")], table) # 统一数据格式：患有疾病为1，没有疾病为0 data[, c("CHD", "CHF", "stroke", "angina", "heart_attack")] <- sapply( data[, c("CHD", "CHF", "stroke", "angina", "heart_attack")], function(x) { x[x == 9] <- NA x[x == 7] <- NA x[x == 2] <- 0 x[x == 1] <- 1 x[x == "no"] <- 0 x[x == "yes"] <- 1 x[x == "don't know"] <- NA x[x == "refused"] <- NA x } ) ## 定义CVD：患有心血管疾病为1，没有心血管疾病为0 #### data$CVD <- case_when( data$CHD == "1"|data$CHF == "1"|data$stroke == "1"|data$angina == "1"|data$heart_attack == "1" ~ 1, data$CHD == "0" & data$CHF == "0" & data$stroke == "0" & data$angina == "0" & data$heart_attack == "0" ~ 0, TRUE ~ NA ) table(data$CVD, useNA = "ifany") # ----------- 筛选人群 ----------- ## 清理"age" #### # recode.num(data$age) data$age <- case_when( data$age == ">= 80 years of age" ~ "80", data$age == ">= 85 years of age" ~ "85", data$age == "80 years of age and over" ~ "80", TRUE ~ data$age ) %>% as.numeric() summary(data$age) ## 合并变量-“权重” #### # 转换为数值型 data[,c("WTSAF4YR","WTSAFPRP","WTSAF2YR")] <- sapply(data[,c("WTSAF4YR","WTSAFPRP","WTSAF2YR")], as.numeric) # 合并 data$wts<-coalesce(data$WTSAF4YR,data$WTSAFPRP,data$WTSAF2YR) ## 筛选人群 #### # ≥20岁的成年人且为空腹子样本； data <- subset(data,data$age >= 20 & !is.na(data$wts) & data$wts != 0) # ----------- 定义组分 ----------- ## 1、计算egfr，2021CKD-EPI ##### # cr_mg.dl: 血清肌酐 (mg/dL) # age: 年龄 (岁) # sex: 性别 ("female" 或 "male") ## 定义计算 egfr 的函数 calculate_egfr_2021 <- function(cr_mg.dl, age, sex) { # 如果任意输入为 NA，则返回 NA if (is.na(cr_mg.dl)|is.na(age)|is.na(sex)) { return(NA) } if (sex == "female") { kappa <- 0.7 alpha <- -0.241 sex_coef <- 1.012 } else if (sex == "male") { kappa <- 0.9 alpha <- -0.302 sex_coef <- 1 } else { stop("性别必须是 'female' 或 'male'") } ratio <- cr_mg.dl / kappa egfr <- 142 * (min(ratio, 1)^alpha) * (max(ratio, 1)^(-1.200)) * (0.9938^age) * sex_coef return(egfr) } # 使用 dplyr 的 mutate 函数计算 egfr data <- data %>% mutate( egfr = mapply(calculate_egfr_2021, cr_mg.dl, age, sex) ) ## 2、定义糖尿病（DM） ##### ### 清理"DM_told"，Label: doctor told you have diabetes ##### table(data$DM_told, useNA = "ifany") data$DM_told <- case_when( data$DM_told == "yes" ~ 1, data$DM_told == "no" ~ 0, data$DM_told == "borderline" ~ 0, TRUE ~ NA) table(data$DM_told, useNA = "ifany") ### 清理“DM_pills_now”，Label: take diabetic pills to lower blood sugar ##### table(data$DM_pills_now,useNA = "ifany") # recode.chr(data$DM_pills_now) data$DM_pills_now <- case_when( data$DM_pills_now == "no" ~ 0, data$DM_pills_now == "yes" ~ 1, data$DM_pills_now == "don't know" ~ NA, TRUE ~ NA) table(data$DM_pills_now, useNA = "ifany") # 处理填补“现在是否用降糖药”的缺失，用“DM_told”填补 data$DM_pills_now <- case_when( !is.na(data$DM_pills_now) ~ data$DM_pills_now, # 如果 DM_pills_now 不缺失，保持原值 !is.na(data$DM_told) ~ 0, # 如果 DM_pills_now 缺失但 DM_told 不缺失，填补为 0 TRUE ~ NA # 其他情况保持缺失 ) table(data$DM_pills_now,useNA = "ifany") ### 清理“DM_insulin_now”，Label: taking insulin now ##### table(data$DM_insulin_now,useNA = "ifany") # recode.chr(data$DM_insulin_now) data$DM_insulin_now <- case_when( data$DM_insulin_now == "yes" ~ 1, data$DM_insulin_now == "no" ~ 0, data$DM_insulin_now == "don't know" ~ NA, TRUE ~ NA) table(data$DM_insulin_now,useNA = "ifany") # 处理填补“现在是否用胰岛素”的缺失，用“JB_DM_told”填补 data$DM_insulin_now <- case_when( !is.na(data$DM_insulin_now) ~ data$DM_insulin_now, # 如果 DM_insulin_now 不缺失，保持原值 !is.na(data$DM_told) ~ 0, # 如果 DM_insulin_now 缺失但 DM_told 不缺失，填补为 0 TRUE ~ NA # 其他情况保持缺失 ) table(data$DM_insulin_now,useNA = "ifany") ### 定义糖尿病（DM） ##### # 定义人群仅为空腹亚组样本，故考虑空腹血糖≥126 mg/dL # 符合以下任一条件：被诊断为糖尿病;服用降血糖药物;注射胰岛素;空腹血糖≥126 mg/dL;糖血红蛋白≥6.5%，则DM为1，否则为0 data$DM <- case_when( data$DM_told == 1 ~ 1, data$DM_pills_now == 1 ~ 1, data$DM_insulin_now == 1 ~ 1, data$fpg_mg.dl >= 126 ~ 1, data$HbA1c >= 6.5 ~ 1, data$DM_told == 0 & data$DM_pills_now == 0 & data$DM_insulin_now == 0 & data$fpg_mg.dl < 126 & data$HbA1c < 6.5 ~ 0, TRUE ~ NA) table(data$DM, useNA = "ifany") ## 3、计算收缩压、舒张压均值 #### #1999-2016年：采用汞血压计（汞血压计：每人至少连续测量三次，如果血压测量中断或不完整，可能会进行第四次尝试）； #2017-2018年：采用汞血压计和电子血压计（电子血压计：每人至少连续测量三次）； #2017-2020年、2021-2023年：采用电子血压计。 ### 采用汞血压计：计算收缩压、舒张压均值，取至少三次测量值的平均值 ##### data$SBP_avg <- apply(data[, c("BPXSY1", "BPXSY2", "BPXSY3", "BPXSY4")], #选择所有行以及需要的列 1, #1是按行计算，2是按列计算 function(x) ifelse(sum(!is.na(x)) >= 3, mean(x, na.rm = TRUE), NA)) #FUN：要应用的函数，比如ifelse data$DBP_avg <- apply(data[, c("BPXDI1", "BPXDI2", "BPXDI3", "BPXDI4")], #选择所有行以及需要的列 1, #1是按行计算，2是按列计算 function(x) ifelse(sum(!is.na(x)) >= 3, mean(x, na.rm = TRUE), NA)) #FUN：要应用的函数，比如ifelse ### 采用电子血压计：计算收缩压、舒张压均值 ##### data$SBPO_avg <- apply(data[, c("BPXOSY1", "BPXOSY2", "BPXOSY3")], 1, #1是按行计算，2是按列计算 function(x) ifelse(sum(!is.na(x)) == 3, mean(x, na.rm = TRUE), NA)) #FUN：要应用的函数，比如ifelse data$DBPO_avg <- apply(data[, c("BPXODI1", "BPXODI2", "BPXODI3")], 1, #1是按行计算，2是按列计算 function(x) ifelse(sum(!is.na(x)) == 3, mean(x, na.rm = TRUE), NA)) #FUN：要应用的函数，比如ifelse ### 合并收缩压、舒张压均值，2017年-2018年采用汞血压计和电子血压计两种方法，本次采用电子血压计的数据 ##### data$SBP <- coalesce(data$SBPO_avg,data$SBP_avg ) data$DBP <- coalesce(data$DBPO_avg,data$DBP_avg ) ## 4、定义高血压（HTN） ##### ### 清理“HBP_told”，Label: ever told you had high blood pressure ##### table(data$HBP_told,useNA = "ifany") # recode.chr(data$HBP_told) data$HBP_told <- case_when( data$HBP_told == "yes" ~ 1, data$HBP_told == "no" ~ 0, data$HBP_told == "don't know" ~ NA, TRUE ~ NA) table(data$HBP_told,useNA = "ifany") ### 清理“HBP_told_treat”，Label: taking prescription for hypertension-被告知是否需要服药 #### table(data$HBP_told_treat,useNA = "ifany") # recode.chr(data$BPQ040A) data$HBP_told_treat <- case_when( data$HBP_told_treat == "yes" ~ "1", data$HBP_told_treat == "no" ~ "0", data$HBP_told_treat == "don't know" ~ NA, data$HBP_told_treat == "refused" ~ NA, TRUE ~ NA_character_ ) table(data$HBP_told_treat, useNA = "ifany") ### 清理“HBP_treat”，Label: now taking prescribed medicine for hbp ##### table(data$HBP_treat,useNA = "ifany") # recode.chr(data$HBP_treat) data$HBP_treat <- case_when( data$HBP_treat == "yes" ~ 1, data$HBP_treat == "no" ~ 0, data$HBP_treat == "don't know" ~ NA, TRUE ~ NA ) table(data$HBP_treat,useNA = "ifany") # 处理填补“现在是否服用降压药”的缺失，用“HBP_told”填补 data$HBP_treat <- case_when( !is.na(data$HBP_treat) ~ data$HBP_treat, # 如果 HBP_treat 不缺失，保持原值 data$HBP_told == 0 ~ 0, data$HBP_told_treat == 0 ~ 0, TRUE ~ NA) # 其他情况保持缺失 table(data$HBP_treat,useNA = "ifany") ### 清理“HBP_told2time”，Label: told had high blood pressure-2+ times ##### table(data$HBP_told2time,useNA = "ifany") # recode.chr(data$HBP_told2time) data$HBP_told2time <- case_when( data$HBP_told2time == "yes" ~ 1, data$HBP_told2time == "no" ~ 0, data$HBP_told2time == "don't know" ~ NA, data$HBP_told2time == "refused" ~ NA, TRUE ~ NA ) table(data$HBP_told2time,useNA = "ifany") # 处理填补“被告知高血压2次”的缺失，用“HBP_told”填补 data$HBP_told2time <- case_when( !is.na(data$HBP_told2time) ~ data$HBP_told2time, # 如果 HBP_told2time 不缺失，保持原值 !is.na(data$HBP_told) ~ 0, # 如果 HBP_told2time 缺失但 HBP_told 不缺失，填补为 0 TRUE ~ NA) # 其他情况保持缺失 table(data$HBP_told2time,useNA = "ifany") table(data$HBP_told2time,data$HBP_treat) ### 定义高血压（HTN） ###### # 符合以下任一条件：被告知高血压2次;是否服用降压药；收缩压≥130 mmHg或舒张压≥80 mmHg;则HTN为1，否则为0 data$HTN <- case_when( data$HBP_told2time == 1 ~ 1, data$HBP_treat == 1 ~ 1, data$SBP >= 130 ~ 1, data$DBP >= 80 ~ 1, data$HBP_told2time == 0 & data$HBP_treat == 0 & data$SBP < 130 & data$DBP < 80 ~ 0, TRUE ~ NA) table(data$HTN, useNA = "ifany") ## 5、定义BMI升高（elevated BMI） ##### # >25 kg/m2 data$elevated_BMI <- case_when( data$BMI > 25 ~ 1, !is.na(data$BMI) ~ 0, TRUE ~ NA) table(data$elevated_BMI, useNA = "ifany") ## 6、定义糖尿病前期（prediabetes） ##### # 糖化血红蛋⽩为 5.7% ⾄ <6.5% 或空腹血糖为 100 mg/dL ⾄ <126 mg/dL data$prediabetes <- case_when( data$HbA1c >= 5.7 & data$HbA1c < 6.5 ~ 1, data$fpg_mg.dl >= 100 & data$fpg_mg.dl < 126 ~ 1, !is.na(data$HbA1c)&!is.na(data$fpg_mg.dl) ~ 0, TRUE ~ NA) table(data$prediabetes, useNA = "ifany") ## 7、定义代谢综合征（MetS） ##### data <- data %>% mutate( # 条件 1: 腰围升高（女性和男性分别≥88 cm 和≥102 cm） elevated_wc = case_when( sex == "female" & waist_cir >= 88 ~ 1, sex == "male" & waist_cir >= 102 ~ 1, !is.na(waist_cir) ~ 0, TRUE ~ NA ), # 条件 2: 低 HDL(男性和女性分别为<40 mg/dL和<50 mg/dL) low_hdl = case_when( sex == "male" & HDLC_mg.dl < 40 ~ 1, sex == "female" & HDLC_mg.dl < 50 ~ 1, !is.na(HDLC_mg.dl) ~ 0, TRUE ~ NA ), # 条件 3: 空腹甘油三酯 ≥ 150 mg/dL high_tg = case_when( ftg_mg.dl >= 150 ~ 1, !is.na(ftg_mg.dl) ~ 0, TRUE ~ NA ), # 条件 4: 血压升高（收缩压≥130 mmHg或舒张压≥80 mmHg或使用降压药） elevated_bp = case_when( SBP >= 130 ~ 1, DBP >= 80 ~ 1, HBP_treat == 1 ~ 1, SBP < 130 & DBP < 80 & HBP_treat == 0 ~ 0, TRUE ~ NA ), # 条件 5: 糖尿病前期 prediabetes = prediabetes, # 计算 MetS mets_count = rowSums( cbind(elevated_wc, low_hdl, high_tg, elevated_bp, prediabetes), na.rm = TRUE ), MetS = ifelse( mets_count >= 3, 1, ifelse( rowSums(is.na(cbind(elevated_wc, low_hdl, high_tg, elevated_bp, prediabetes))) > 0, NA, 0 ) ) ) table(data$elevated_wc, useNA = "ifany") table(data$low_hdl, useNA = "ifany") table(data$high_tg, useNA = "ifany") table(data$elevated_bp, useNA = "ifany") table(data$prediabetes, useNA = "ifany") table(data$MetS, useNA = "ifany") ## 8、定义KDIGO CKD分期 ##### data <- data %>% mutate( CKD = case_when( # 极高风险（红色） egfr < 30 ~ "Very High", # 无论ucrea或ualb是否缺失 egfr >= 30 & egfr < 45 & (ualb_ug.ml * 100 / ucrea_mg.dl) >= 30 ~ "Very High", egfr >= 45 & egfr < 60 & (ualb_ug.ml * 100 / ucrea_mg.dl) > 300 ~ "Very High", # 中度或高度风险（黄/橙） egfr >= 30 & egfr < 45 & (ualb_ug.ml * 100 / ucrea_mg.dl) < 30 ~ "Moderate to High", egfr >= 45 & egfr < 60 & (ualb_ug.ml * 100 / ucrea_mg.dl) <= 300 ~ "Moderate to High", egfr >= 60 & (ualb_ug.ml * 100 / ucrea_mg.dl) >= 30 ~ "Moderate to High", # 低风险（绿色） egfr >= 60 & (ualb_ug.ml * 100 / ucrea_mg.dl) < 30 ~ "Low", TRUE ~ NA_character_ ) ) table(data$CKD, useNA = "ifany") ## 9、定义10年CVD风险 ##### # Input variables Allowable range # Age, years 30-79 years # Total cholesterol (TC), mg/dl 130 to 320 mg/dl # High-density lipoprotein cholesterol (HDL-C), mg/dl 20 to 100 mg/dl # Systolic blood pressure (SBP), mmHg 90 to 200 mmHg # Diabetes, yes=1 no=0 0 or 1 # Current smoking (Cursmk), yes=1 no=0 0 or 1 # Estimated GFR (eGFR), ml/min/1.73m2 15 to 150 ml/min/1.73m2 # Anti-hypertension medication use (antihypertensive), yes=1 no=0 0 or 1 # Statin use, yes=1 no=0 0 or 1 # 处理age data$age<-ifelse(data$age>=79,79, ifelse(data$age<=30,30,data$age)) # 处理TC data$TC_mg.dl<-ifelse(data$TC_mg.dl>=320,320, ifelse(data$TC_mg.dl<=130,130,data$TC_mg.dl)) # 处理HDL data$HDLC_mg.dl<-ifelse(data$HDLC_mg.dl>=100,100, ifelse(data$HDLC_mg.dl<=20,20,data$HDLC_mg.dl)) # 处理SBP data$SBP<-ifelse(data$SBP>=200,200, ifelse(data$SBP<=90,90,data$SBP)) # 处理DM data$DM <- data$DM # 处理smoker data$Cursmk <- case_when( data$SMQ040 == "not at all" ~ "0", data$SMQ040 == "every day" ~ "1", data$SMQ040 == "some days" ~ "1", data$SMQ040 == "not at all?" ~ "0", data$SMQ040 == "every day," ~ "1", data$SMQ040 == "some days, or" ~ "1", data$SMQ020 == "no" ~ "0", #填缺失 TRUE ~ NA ) %>% as.numeric() table(data$Cursmk, useNA = "ifany") # 处理eGFR data$egfr<-ifelse(data$egfr>=150,150, ifelse(data$egfr<=15,15,data$egfr)) # 处理antihypertensive-使用降压药 data$HBP_treat<-data$HBP_treat # 处理statin-使用他汀类药物 data$statin_I <- data$statin_I # 定义函数 prevent_10risk <- function(age, TC, HDL, SBP, eGFR, diabetes, smoker, BMI, antihypertensive, statin, gender) { # Initialize log_odds variable log_odds <- 0 # CVD risk calculation (Women) if (gender == "female") { log_odds <- -3.307728 + 0.7939329 * (age - 55) / 10 + 0.0305239 * ((TC - HDL) * 0.02586 - 3.5) - 0.1606857 * (HDL * 0.02586 - 1.3) / 0.3 - 0.2394003 * (min(SBP, 110) - 110) / 20 + 0.360078 * (max(SBP, 110) - 130) / 20 + 0.8667604 * diabetes + 0.5360739 * smoker + 0.6045917 * (min(eGFR, 60) - 60) / -15 + 0.0433769 * (max(eGFR, 60) - 90) / -15 + 0.3151672 * antihypertensive - 0.1477655 * statin - 0.0663612 * antihypertensive * (max(SBP, 110) - 130) / 20 + 0.1197879 * statin * ((TC - HDL) * 0.02586 - 3.5) - 0.0819715 * (age - 55) / 10 * ((TC - HDL) * 0.02586 - 3.5) + 0.0306769 * (age - 55) / 10 * (HDL * 0.02586 - 1.3) / 0.3 - 0.0946348 * (age - 55) / 10 * (max(SBP, 110) - 130) / 20 - 0.27057 * (age - 55) / 10 * diabetes - 0.078715 * (age - 55) / 10 * smoker - 0.1637806 * (age - 55) / 10 * (min(eGFR, 60) - 60) / -15 } # CVD risk calculation (Men) if (gender == "male") { log_odds <- -3.031168 + 0.7688528 * (age - 55) / 10 + 0.0736174 * ((TC - HDL) * 0.02586 - 3.5) - 0.0954431 * (HDL * 0.02586 - 1.3) / 0.3 - 0.4347345 * (min(SBP, 110) - 110) / 20 + 0.3362658 * (max(SBP, 110) - 130) / 20 + 0.7692857 * diabetes + 0.4386871 * smoker + 0.5378979 * (min(eGFR, 60) - 60) / -15 + 0.0164827 * (max(eGFR, 60) - 90) / -15 + 0.288879 * antihypertensive - 0.1337349 * statin - 0.0475924 * antihypertensive * (max(SBP, 110) - 130) / 20 + 0.150273 * statin * ((TC - HDL) * 0.02586 - 3.5) - 0.0517874 * (age - 55) / 10 * ((TC - HDL) * 0.02586 - 3.5) + 0.0191169 * (age - 55) / 10 * (HDL * 0.02586 - 1.3) / 0.3 - 0.1049477 * (age - 55) / 10 * (max(SBP, 110) - 130) / 20 - 0.2251948 * (age - 55) / 10 * diabetes - 0.0895067 * (age - 55) / 10 * smoker - 0.1543702 * (age - 55) / 10 * (min(eGFR, 60) - 60) / -15 } # Calculate risk risk <- exp(log_odds) / (1 + exp(log_odds)) return(risk) } # 计算10年CVD风险 data$CVD_10risk <- mapply(prevent_10risk, age = data$age, TC = data$TC_mg.dl, HDL = data$HDLC_mg.dl, SBP = data$SBP, eGFR = data$egfr, diabetes = data$DM, smoker = data$Cursmk, antihypertensive = data$HBP_treat, statin = data$statin_I, gender = data$sex ) summary(data$CVD_10risk) # ----------- 定义CKM ----------- data <- data %>% mutate( CKM_stage = case_when( # Stage 4：自报 CVD 存在 CVD == 1 ~ 4, # Stage 3：非常高风险 CKD 或 10 年 CVD 风险 ≥20% CKD == "Very High"|CVD_10risk >= 0.20 ~ 3, # Stage 2：中度⾄⾼度风险 CKD或存在代谢风险因素 # 例如：空腹甘油三酯 ≥135 mg/dL 或高血压（HTN）、糖尿病（DM）、代谢综合征（MetS==1） CKD == "Moderate to High" |ftg_mg.dl >= 135|HTN == 1|DM == 1|MetS == 1 ~ 2, # Stage 1：存在轻度异常（BMI/腰围异常或糖尿病前期） elevated_BMI == 1|elevated_wc == 1|prediabetes == 1 ~ 1, # # Stage 0：上述条件均不满足 CVD == 0 & CKD == "Low" & CVD_10risk < 0.20 & ftg_mg.dl < 135 & HTN == 0 & DM == 0 & MetS == 0 & elevated_BMI == 0 & elevated_wc == 0 & prediabetes == 0 ~ 0, TRUE ~ NA ) ) table(data$CKM_stage, useNA = "ifany") ## 相关文献 ##### # 38717747 # ----------- 创建新字典 ----------- ## 读取原始变量字典文件（包含原始变量名、标签等元数据信息），将新生成的变量也加入到字典 #### name_z <- read.csv("raw_codebook.csv") names(name_z) ## 变量映射表：将新生成的变量与原始变量进行映射，便于后续生成字典 #### add_mapping <- function(map, new_var, original_var) { # 将向量转换为逗号分隔的字符串存储 original_var_str <- paste(original_var, collapse = ", ") rbind(map, data.frame(Variable = new_var, original_vars = original_var_str, stringsAsFactors = FALSE)) } map <- data.frame(Variable = character(), original_vars = character()) map <- add_mapping(map, "CVD", c("CHD","CHF","heart_attack","stroke","angina")) map <- add_mapping(map, "wts", c("WTSAF4YR","WTSAFPRP","WTSAF2YR")) map <- add_mapping(map, "egfr", c("cr_mg.dl","age","sex")) map <- add_mapping(map, "DM", c("DM_told","DM_pills_now","DM_insulin_now","HbA1c","fpg_mg.dl")) map <- add_mapping(map, "SBP_avg", c("BPXSY1","BPXSY2","BPXSY3","BPXSY4")) map <- add_mapping(map, "DBP_avg", c("BPXDI1","BPXDI2","BPXDI3","BPXDI4")) map <- add_mapping(map, "SBPO_avg", c("BPXOSY1","BPXOSY2","BPXOSY3")) map <- add_mapping(map, "DBPO_avg", c("BPXODI1","BPXODI2","BPXODI3")) map <- add_mapping(map, "SBP", c("BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3")) map <- add_mapping(map, "DBP", c("BPXDI1","BPXDI2","BPXDI3","BPXDI4","BPXODI1","BPXODI2","BPXODI3")) map <- add_mapping(map, "HTN", c("HBP_told","HBP_told2time","HBP_treat","BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3","BPXDI1","BPXDI2","BPXDI3","BPXDI4","BPXODI1","BPXODI2","BPXODI3")) map <- add_mapping(map, "elevated_BMI", c("BMI")) map <- add_mapping(map, "prediabetes", c("fpg_mg.dl","HbA1c")) map <- add_mapping(map, "elevated_wc", c("waist_cir","sex")) map <- add_mapping(map, "low_hdl", c("sex","HDLC_mg.dl")) map <- add_mapping(map, "high_tg", c("ftg_mg.dl")) map <- add_mapping(map, "elevated_bp", c("HBP_treat","BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3","BPXDI1","BPXDI2","BPXDI3","BPXDI4","BPXODI1","BPXODI2","BPXODI3")) map <- add_mapping(map, "mets_count", c("waist_cir","sex","HDLC_mg.dl","ftg_mg.dl","HBP_treat","BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3","BPXDI1","BPXDI2","BPXDI3","BPXDI4","BPXODI1","BPXODI2","BPXODI3","fpg_mg.dl","HbA1c")) map <- add_mapping(map, "MetS", c("waist_cir","sex","HDLC_mg.dl","ftg_mg.dl","HBP_treat","BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3","BPXDI1","BPXDI2","BPXDI3","BPXDI4","BPXODI1","BPXODI2","BPXODI3","fpg_mg.dl","HbA1c")) map <- add_mapping(map, "CKD", c("cr_mg.dl","age","sex","ualb_ug.ml","ucrea_mg.dl")) map <- add_mapping(map, "Cursmk", c("SMQ040","SMQ020")) map <- add_mapping(map, "CVD_10risk", c("age","TC_mg.dl","HDLC_mg.dl","BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3","cr_mg.dl","sex","DM_told","DM_pills_now","DM_insulin_now","HbA1c","fpg_mg.dl","SMQ040","SMQ020","HBP_treat","STATNS_I")) map <- add_mapping(map, "CKM_stage", c("WTSAF4YR","WTSAFPRP","WTSAF2YR","CHD","CHF","heart_attack","stroke","angina","cr_mg.dl","age","sex","ualb_ug.ml","ucrea_mg.dl","TC_mg.dl","HDLC_mg.dl","BPXSY1","BPXSY2","BPXSY3","BPXSY4","BPXOSY1","BPXOSY2","BPXOSY3","DM_told","DM_pills_now","DM_insulin_now","HbA1c","fpg_mg.dl","SMQ040","SMQ020","HBP_treat","STATNS_I","ftg_mg.dl","HBP_told","HBP_told2time","BPXDI1","BPXDI2","BPXDI3","BPXDI4","BPXODI1","BPXODI2","BPXODI3","waist_cir","BMI")) ## 创建codebook字典：为每个变量（包括新生成的变量）匹配原始字典信息，生成包含变量名、原始变量名、标签等信息的字典表 #### codebook <- lapply(names(data), function(col) { col_var <- col # 获取原始变量列表（如果是映射变量） if (col %in% map$Variable) { original_vars_str <- map$original_vars[map$Variable == col] original_vars_list <- unlist(strsplit(original_vars_str, ", ")) } else { original_vars_list <- col } # 对每个原始变量查找匹配项 matches_list <- lapply(original_vars_list, function(orig_var) { name_z[grepl(orig_var, name_z$newname) & sapply(gsub(orig_var, "", name_z$newname), function(x) stringr::str_count(x, "_") <= 1), ] }) # 过滤掉空的数据框 matches_list <- matches_list[sapply(matches_list, nrow) > 0] if (length(matches_list) > 0) { matches <- do.call(rbind, matches_list) matches <- matches[!duplicated(matches), ] # 去除重复 } else { matches <- data.frame(Variable = character(), newname = character(), Label = character(), stringsAsFactors = FALSE) } data.frame( Variable = col_var, original_vars = if(nrow(matches) > 0) paste(unique(matches$Variable), collapse = ", ") else "", processed_vars = if(nrow(matches) > 0) paste(unique(matches$newname), collapse = ", ") else "", Label = if(nrow(matches) > 0) first(matches$Label) else "", count = if(nrow(matches) > 0) nrow(matches) else 0, stringsAsFactors = FALSE ) }) %>% bind_rows() View(codebook) ## 新生成的变量需要手动标注意思!!!! #### codebook$Label[codebook$Variable == "statin_I"] <- "Statin use: identified by ingredient" codebook$Label[codebook$Variable == "statin_N"] <- "Statin use: identified by brand name" codebook$Label[codebook$Variable == "statin_I_N"] <- "Statin use: identified by ingredient and brand name" codebook$Label[codebook$Variable == "CVD"] <- "cardiovascular disease" codebook$Label[codebook$Variable == "wts"] <- "fasting subsample weigh" codebook$Label[codebook$Variable == "egfr"] <- "estimated glomerular filtration rate" codebook$Label[codebook$Variable == "DM"] <- "diabetes" codebook$Label[codebook$Variable == "SBP_avg"] <- "average systolic blood pressure (1999-2016)" codebook$Label[codebook$Variable == "DBP_avg"] <- "average diastolic blood pressure (1999-2016)" codebook$Label[codebook$Variable == "SBPO_avg"] <- "average systolic blood pressure (2017-2023)" codebook$Label[codebook$Variable == "DBPO_avg"] <- "average diastolic blood pressure (2017-2023)" codebook$Label[codebook$Variable == "SBP"] <- "average systolic blood pressure (1999-2023)" codebook$Label[codebook$Variable == "DBP"] <- "average diastolic blood pressure (1999-2023)" codebook$Label[codebook$Variable == "HTN"] <- "hypertension" codebook$Label[codebook$Variable == "elevated_BMI"] <- " > 25 kg/m²" codebook$Label[codebook$Variable == "elevated_wc"] <- " ≥ 88 cm for women and ≥ 102 cm for men" codebook$Label[codebook$Variable == "prediabetes"] <- "glycated hemoglobin of 5.7% to < 6.5% or fasting blood glucose of 100 mg/dL to < 126 mg/dL" codebook$Label[codebook$Variable == "low_hdl"] <- "< 40 mg/dL or < 50 mg/dL for men or women, respectively" codebook$Label[codebook$Variable == "high_tg"] <- "fasting serum triglycerides ≥ 150 mg/dL" codebook$Label[codebook$Variable == "elevated_bp"] <- "systolic blood pressure ≥ 130mmHg, diastolic blood pressure ≥ 80 mmHg, or use of blood pressure-lowering medications" codebook$Label[codebook$Variable == "mets_count"] <- "Count of metabolic syndrome components (0-5 points, based on IDF criteria: elevated waist circumference, low HDL-C, high triglycerides, elevated blood pressure, and prediabetes" codebook$Label[codebook$Variable == "MetS"] <- "Metabolic syndrome status (1 = metabolic syndrome diagnosed, 0 = not diagnosed, NA = unable to determine due to missing data; diagnostic threshold: ≥3 abnormal components" codebook$Label[codebook$Variable == "CKD"] <- "KDIGO CKD stages:very high risk, moderate to high risk, low risk" codebook$Label[codebook$Variable == "Cursmk"] <- "current smoker" codebook$Label[codebook$Variable == "CVD_10risk"] <- "10-year cardiovascular disease risk" codebook$Label[codebook$Variable == "CKM_stage"] <- "CKM stages 0-4" write.xlsx(data, "db_CKM.xlsx") write.xlsx(codebook, "codebook_CKM.xlsx") ## 输出文件以分析数据 #### analysis_data <- data[c("SEQN","CKM_stage")] analysis_codebook <- subset(codebook, Variable %in% c("CKM_stage")) write.xlsx(analysis_data, "analysis_db_CKM_2.xlsx") write.xlsx(analysis_codebook, "analysis_codebook_CKM_2.xlsx") ``` ## 定义的组分概览

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Variable	original_vars	easylabel	1999-2000	2001-2002	2003-2004	2005-2006	2007-2008	2009-2010	2011-2012	2013-2014	2015-2016	2017-2018	2017-2020	2021-2023
Basic Variables
CHF	MCQ160B	ever told had congestive heart failure	1,849	2,198	1,966	1,975	2,350	2,615	2,311	2,390	2,266	2,287	3,789	2,954
angina	MCQ160D	ever told you had angina/angina pectoris	1,847	2,193	1,970	1,974	2,348	2,612	2,311	2,388	2,265	2,281	3,783	2,952
CHD	MCQ160C	ever told you had coronary heart disease	1,846	2,190	1,969	1,973	2,350	2,611	2,306	2,387	2,259	2,284	3,785	2,951
heart_attack	MCQ160E	ever told you had heart attack	1,849	2,203	1,976	1,979	2,356	2,617	2,313	2,389	2,266	2,288	3,794	2,955
stroke	MCQ160F	ever told you had a stroke	1,853	2,201	1,976	1,980	2,355	2,621	2,313	2,389	2,268	2,292	3,796	2,955
sex	RIAGENDR	gender	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
age	RIDAGEYR	age in years at screening	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
WTSAFPRP	WTSAFPRP	fasting subsample weight											3,801
WTSAF2YR	WTSAF2YR	fasting subsample 2 year mec weight	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295		2,958
WTSAF4YR	WTSAF4YR	fasting subsample 4 year mec weight	1,854	2,205
cr_mg.dl	LBXSCR, LBDSCR	creatinine (mg/dl)	1,823	2,168	1,950	1,962	2,338	2,589	2,279	2,359	2,252	2,251	3,720	2,809
DM_told	DIQ010	doctor told you have diabetes	1,853	2,205	1,977	1,979	2,358	2,621	2,313	2,390	2,269	2,293	3,799	2,958
DM_pills_now	DIQ070, DID070	take diabetic pills to lower blood sugar	1,853	2,205	1,977	1,979	2,358	2,621	2,313	2,390	2,269	2,293	3,799	2,958
DM_insulin_now	DIQ050	taking insulin now	1,853	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,799	2,958
fpg_mg.dl	LBXGLU	fasting glucose (mg/dl)	1,847	2,200	1,965	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
HbA1c	LBXGH	glycohemoglobin (%)	1,849	2,200	1,967	1,973	2,352	2,615	2,310	2,388	2,269	2,292	3,791	2,956
BPXSY1	BPXSY1	systolic: blood pres (1st rdg) mm hg	1,659	1,989	1,641	1,747	2,217	2,435	2,129	2,205	2,150	2,032
BPXSY2	BPXSY2	systolic: blood pres (2nd rdg) mm hg	1,613	1,920	1,546	1,652	2,180	2,468	2,179	2,283	2,214	2,156
BPXSY3	BPXSY3	systolic: blood pres (3rd rdg) mm hg	1,612	1,895	1,538	1,629	2,142	2,466	2,184	2,295	2,205	2,147
BPXSY4	BPXSY4	systolic: blood pres (4th rdg) mm hg	375	363	414	290	64	111	157	146	108	200
BPXOSY1	BPXOSY1	systolic-1st oscillometric reading										2,030	3,458	2,863
BPXOSY2	BPXOSY2	systolic-2nd oscillometric reading										2,027	3,455	2,860
BPXOSY3	BPXOSY3	systolic-3rd oscillometric reading										2,021	3,447	2,852
BPXDI1	BPXDI1	diastolic: blood pres (1st rdg) mm hg	1,659	1,989	1,641	1,747	2,217	2,435	2,129	2,205	2,150	2,032
BPXDI2	BPXDI2	diastolic: blood pres (2nd rdg) mm hg	1,613	1,920	1,546	1,652	2,180	2,468	2,179	2,283	2,214	2,156
BPXDI3	BPXDI3	diastolic: blood pres (3rd rdg) mm hg	1,612	1,895	1,538	1,629	2,142	2,466	2,184	2,295	2,205	2,147
BPXDI4	BPXDI4	diastolic: blood pres (4th rdg) mm hg	375	363	414	290	64	111	157	146	108	200
BPXODI1	BPXODI1	diastolic-1st oscillometric reading										2,030	3,458	2,863
BPXODI2	BPXODI2	diastolic-2nd oscillometric reading										2,027	3,455	2,860
BPXODI3	BPXODI3	diastolic-3rd oscillometric reading										2,021	3,447	2,852
HBP_told	BPQ020, BPQ030	ever told you had high blood pressure	1,836	2,179	1,958	1,979	2,357	2,619	2,311	2,388	2,267	2,291	3,796	2,956
HBP_treat	BPQ050A, BPQ150	now taking prescribed medicine for hbp	1,836	2,179	1,958	1,979	2,357	2,619	2,311	2,388	2,267	2,291	3,796	2,956
HBP_told2time	BPQ030	told had high blood pressure-2+ times	1,836	2,179	1,958	1,979	2,357	2,619	2,311	2,388	2,267	2,291	3,796	2,956
BMI	BMXBMI	body mass index (kg/m**2)	1,842	2,118	1,947	1,947	2,319	2,600	2,288	2,368	2,252	2,258	3,742	2,925
waist_cir	BMXWAIST	waist circumference (cm)	1,829	2,129	1,904	1,913	2,272	2,547	2,238	2,298	2,170	2,192	3,652	2,839
ftg_mg.dl	LBXTR, LBXTLG	triglyceride (mg/dl)	1,819	2,163	1,952	1,959	2,347	2,595	2,286	2,365	2,254	2,258	3,737	2,838
HDLC_mg.dl	LBDHDL, LBXHDD, LBDHDD	hdl-cholesterol (mg/dl)	1,820	2,163	1,956	1,967	2,350	2,597	2,287	2,366	2,256	2,258	3,737	2,840
ualb_ug.ml	URXUMA	albumin, urine (ug/ml)	1,836	2,172	1,942	1,960	2,317	2,596	2,294	2,362	2,246	2,266	3,748	2,902
ucrea_mg.dl	URXUCR	creatinine, urine (mg/dl)	1,836	2,171	1,942	1,960	2,317	2,596	2,294	2,362	2,246	2,266	3,748	2,902
TC_mg.dl	LBXTC	total cholesterol (mg/dl)	1,819	2,163	1,956	1,967	2,350	2,597	2,287	2,366	2,256	2,258	3,737	2,840
SMQ020	SMQ020	smoked at least 100 cigarettes in life	1,854	2,204	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
SMQ040	SMQ040	do you now smoke cigarettes	890	1,075	971	944	1,103	1,160	983	1,037	998	963	1,611	1,156
statin_I_N		Statin use: identified by ingredient and brand name	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
statin_N		Statin use: identified by brand name	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
statin_I		Statin use: identified by ingredient	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
Defining Components
CVD	MCQ160C, MCQ160B, MCQ160E, MCQ160F, MCQ160D	cardiovascular disease	1,841	2,186	1,970	1,974	2,348	2,607	2,310	2,386	2,263	2,276	3,776	2,948
wts	WTSAF4YR, WTSAFPRP, WTSAF2YR	fasting subsample weigh	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
egfr	LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR	estimated glomerular filtration rate	1,823	2,168	1,950	1,962	2,338	2,589	2,279	2,359	2,252	2,251	3,720	2,809
DM	DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU	diabetes	1,852	2,203	1,976	1,973	2,353	2,615	2,311	2,388	2,268	2,290	3,790	2,956
SBP_avg	BPXSY1, BPXSY2, BPXSY3, BPXSY4	average systolic blood pressure (1999-2016)	1,666	1,956	1,537	1,635	2,136	2,470	2,198	2,298	2,222	2,155
DBP_avg	BPXDI1, BPXDI2, BPXDI3, BPXDI4	average diastolic blood pressure (1999-2016)	1,666	1,956	1,537	1,635	2,136	2,470	2,198	2,298	2,222	2,155
SBPO_avg	BPXOSY1, BPXOSY2, BPXOSY3	average systolic blood pressure (2017-2023)										2,021	3,447	2,851
DBPO_avg	BPXODI1, BPXODI2, BPXODI3	average diastolic blood pressure (2017-2023)										2,021	3,447	2,851
SBP	BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3	average systolic blood pressure (1999-2023)	1,666	1,956	1,537	1,635	2,136	2,470	2,198	2,298	2,222	2,239	3,447	2,851
DBP	BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3	average diastolic blood pressure (1999-2023)	1,666	1,956	1,537	1,635	2,136	2,470	2,198	2,298	2,222	2,239	3,447	2,851
HTN	BPQ020, BPQ030, BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3	hypertension	1,712	2,019	1,667	1,751	2,211	2,523	2,231	2,334	2,233	2,258	3,573	2,891
elevated_BMI	BMXBMI	> 25 kg/m²	1,842	2,118	1,947	1,947	2,319	2,600	2,288	2,368	2,252	2,258	3,742	2,925
prediabetes	LBXGLU, LBXGH	glycated hemoglobin of 5.7% to < 6.5% or fasting blood glucose of 100 mg/dL to < 126 mg/dL	1,846	2,200	1,967	1,976	2,353	2,618	2,310	2,389	2,269	2,292	3,794	2,957
elevated_wc	BMXWAIST, RIAGENDR	≥ 88 cm for women and ≥ 102 cm for men	1,829	2,129	1,904	1,913	2,272	2,547	2,238	2,298	2,170	2,192	3,652	2,839
low_hdl	RIAGENDR, LBDHDL, LBXHDD, LBDHDD	< 40 mg/dL or < 50 mg/dL for men or women, respectively	1,820	2,163	1,956	1,967	2,350	2,597	2,287	2,366	2,256	2,258	3,737	2,840
high_tg	LBXTR, LBXTLG	fasting serum triglycerides ≥ 150 mg/dL	1,819	2,163	1,952	1,959	2,347	2,595	2,286	2,365	2,254	2,258	3,737	2,838
elevated_bp	BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3	systolic blood pressure ≥ 130mmHg, diastolic blood pressure ≥ 80 mmHg, or use of blood pressure-lowering medications	1,704	2,000	1,647	1,728	2,202	2,518	2,225	2,330	2,231	2,257	3,559	2,889
mets_count	BMXWAIST, RIAGENDR, LBDHDL, LBXHDD, LBDHDD, LBXTR, LBXTLG, BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, LBXGLU, LBXGH	Count of metabolic syndrome components (0-5 points, based on IDF criteria: elevated waist circumference, low HDL-C, high triglycerides, elevated blood pressure, and prediabetes	1,854	2,205	1,977	1,982	2,359	2,623	2,315	2,390	2,270	2,295	3,801	2,958
MetS	BMXWAIST, RIAGENDR, LBDHDL, LBXHDD, LBDHDD, LBXTR, LBXTLG, BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, LBXGLU, LBXGH	Metabolic syndrome status (1 = metabolic syndrome diagnosed, 0 = not diagnosed, NA = unable to determine due to missing data; diagnostic threshold: ≥3 abnormal components	1,689	1,970	1,651	1,711	2,177	2,474	2,175	2,256	2,154	2,159	3,473	2,746
CKD	LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR, URXUMA, URXUCR	KDIGO CKD stages:very high risk, moderate to high risk, low risk	1,811	2,141	1,920	1,943	2,298	2,566	2,263	2,337	2,232	2,225	3,675	2,761
Cursmk	SMQ040, SMQ020	current smoker	1,851	2,202	1,976	1,979	2,355	2,623	2,312	2,389	2,266	2,295	3,798	2,955
CVD_10risk	RIDAGEYR, LBXTC, LBDHDL, LBXHDD, LBDHDD, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, LBXSCR, LBDSCR, RIAGENDR, DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU, SMQ040, SMQ020, BPQ050A, BPQ150	10-year cardiovascular disease risk	1,619	1,895	1,509	1,604	2,108	2,426	2,155	2,265	2,195	2,186	3,358	2,702
Target Definition
CKM_stage	WTSAF4YR, WTSAFPRP, WTSAF2YR, MCQ160C, MCQ160B, MCQ160E, MCQ160F, MCQ160D, LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR, URXUMA, URXUCR, LBXTC, LBDHDL, LBXHDD, LBDHDD, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU, SMQ040, SMQ020, BPQ050A, BPQ150, LBXTR, LBXTLG, BPQ020, BPQ030, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, BMXWAIST, BMXBMI	CKM stages 0-4	1,819	2,167	1,903	1,929	2,321	2,599	2,289	2,369	2,256	2,281	3,756	2,932

## 定义的组分详情

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Basic Variables

CHF

← MCQ160B

ever told had congestive heart failure

类别分布:

27990	96.7%		0
960	3.3%		1

angina

← MCQ160D

ever told you had angina/angina pectoris

类别分布:

28129	97.3%		0
795	2.7%		1

CHD

← MCQ160C

ever told you had coronary heart disease

类别分布:

27669	95.7%		0
1242	4.3%		1

heart_attack

← MCQ160E

ever told you had heart attack

类别分布:

27710	95.6%		0
1275	4.4%		1

stroke

← MCQ160F

ever told you had a stroke

类别分布:

27877	96.1%		0
1122	3.9%		1

sex

← RIAGENDR

gender

类别分布:

15224	52.4%		female
13805	47.6%		male

age

← RIDAGEYR

age in years at screening

范围:
51.14 (30 ~ 79)

WTSAFPRP

← WTSAFPRP

fasting subsample weight

范围:
63262.6 (4808.07 ~ 741259.19)

WTSAF2YR

← WTSAF2YR

fasting subsample 2 year mec weight

范围:
95632.76 (2631.2 ~ 944153.25)

WTSAF4YR

← WTSAF4YR

fasting subsample 4 year mec weight

范围:
48879.64 (1659.2 ~ 263680.66)

cr_mg.dl

← LBXSCR, LBDSCR

creatinine (mg/dl)

范围:
0.88 (0.2 ~ 17.41)

DM_told

← DIQ010

doctor told you have diabetes

类别分布:

25356	87.4%		0
3659	12.6%		1

DM_pills_now

← DIQ070, DID070

take diabetic pills to lower blood sugar

类别分布:

26043	89.8%		0
2972	10.2%		1

DM_insulin_now

← DIQ050

taking insulin now

类别分布:

28094	96.8%		0
932	3.2%		1

fpg_mg.dl

← LBXGLU

fasting glucose (mg/dl)

范围:
108.6 (21 ~ 587.3)

HbA1c

← LBXGH

glycohemoglobin (%)

范围:
5.74 (2.8 ~ 18)

BPXSY1

← BPXSY1

systolic: blood pres (1st rdg) mm hg

范围:
124.93 (66 ~ 240)

BPXSY2

← BPXSY2

systolic: blood pres (2nd rdg) mm hg

范围:
124.05 (66 ~ 236)

BPXSY3

← BPXSY3

systolic: blood pres (3rd rdg) mm hg

范围:
123.2 (62 ~ 232)

BPXSY4

← BPXSY4

systolic: blood pres (4th rdg) mm hg

范围:
127.83 (74 ~ 238)

BPXOSY1

← BPXOSY1

systolic-1st oscillometric reading

范围:
123.53 (66 ~ 217)

BPXOSY2

← BPXOSY2

systolic-2nd oscillometric reading

范围:
123.41 (65 ~ 222)

BPXOSY3

← BPXOSY3

systolic-3rd oscillometric reading

范围:
123.18 (60 ~ 220)

BPXDI1

← BPXDI1

diastolic: blood pres (1st rdg) mm hg

范围:
69.96 (0 ~ 134)

BPXDI2

← BPXDI2

diastolic: blood pres (2nd rdg) mm hg

范围:
69.64 (0 ~ 134)

BPXDI3

← BPXDI3

diastolic: blood pres (3rd rdg) mm hg

范围:
69.4 (0 ~ 128)

BPXDI4

← BPXDI4

diastolic: blood pres (4th rdg) mm hg

范围:
70.69 (0 ~ 136)

BPXODI1

← BPXODI1

diastolic-1st oscillometric reading

范围:
74.96 (37 ~ 137)

BPXODI2

← BPXODI2

diastolic-2nd oscillometric reading

范围:
74.27 (36 ~ 141)

BPXODI3

← BPXODI3

diastolic-3rd oscillometric reading

范围:
73.89 (37 ~ 135)

HBP_told

← BPQ020, BPQ030

ever told you had high blood pressure

类别分布:

18521	64%		0
10416	36%		1

HBP_treat

← BPQ050A, BPQ150

now taking prescribed medicine for hbp

类别分布:

20971	72.5%		0
7966	27.5%		1

HBP_told2time

← BPQ030

told had high blood pressure-2+ times

类别分布:

20411	70.5%		0
8526	29.5%		1

BMI

← BMXBMI

body mass index (kg/m**2)

范围:
29.19 (11.1 ~ 130.21)

waist_cir

← BMXWAIST

waist circumference (cm)

范围:
99.3 (56.2 ~ 178)

ftg_mg.dl

← LBXTR, LBXTLG

triglyceride (mg/dl)

范围:
128.79 (10 ~ 4233)

HDLC_mg.dl

← LBDHDL, LBXHDD, LBDHDD

hdl-cholesterol (mg/dl)

范围:
53.66 (20 ~ 100)

ualb_ug.ml

← URXUMA

albumin, urine (ug/ml)

范围:
49.54 (0.2 ~ 24440)

ucrea_mg.dl

← URXUCR

creatinine, urine (mg/dl)

范围:
130.78 (3 ~ 805)

TC_mg.dl

← LBXTC

total cholesterol (mg/dl)

范围:
193.52 (130 ~ 320)

SMQ020

← SMQ020

smoked at least 100 cigarettes in life

类别分布:

16110	55.5%	no
12891	44.4%	yes
23	0.1%	don't know
4	0%	refused

SMQ040

← SMQ040

do you now smoke cigarettes

类别分布:

6269	48.6%	not at all
3763	29.2%	every day
1102	8.5%	not at all?
894	6.9%	some days
714	5.5%	every day,
149	1.2%	some days, or

statin_I_N

←

Statin use: identified by ingredient and brand name

类别分布:

24399	84.1%		0
4630	15.9%		1

statin_N

←

Statin use: identified by brand name

类别分布:

24547	84.6%		0
4482	15.4%		1

statin_I

←

Statin use: identified by ingredient

类别分布:

24399	84.1%		0
4630	15.9%		1

Defining Components

CVD

← MCQ160C, MCQ160B, MCQ160E, MCQ160F, MCQ160D

cardiovascular disease

类别分布:

25621	88.7%		0
3264	11.3%		1

wts

← WTSAF4YR, WTSAFPRP, WTSAF2YR

fasting subsample weigh

范围:
84741.34 (1659.2 ~ 944153.25)

egfr

← LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR

estimated glomerular filtration rate

范围:
95.38 (15 ~ 150)

← DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU

diabetes

类别分布:

23687	81.7%		0
5288	18.3%		1

SBP_avg

← BPXSY1, BPXSY2, BPXSY3, BPXSY4

average systolic blood pressure (1999-2016)

范围:
124.04 (64.67 ~ 238)

DBP_avg

← BPXDI1, BPXDI2, BPXDI3, BPXDI4

average diastolic blood pressure (1999-2016)

范围:
69.77 (0 ~ 132)

SBPO_avg

← BPXOSY1, BPXOSY2, BPXOSY3

average systolic blood pressure (2017-2023)

范围:
123.35 (74.67 ~ 214)

DBPO_avg

← BPXODI1, BPXODI2, BPXODI3

average diastolic blood pressure (2017-2023)

范围:
74.37 (37.33 ~ 137.67)

SBP

← BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3

average systolic blood pressure (1999-2023)

范围:
123.66 (90 ~ 200)

DBP

← BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3

average diastolic blood pressure (1999-2023)

范围:
71.01 (0 ~ 137.67)

HTN

← BPQ020, BPQ030, BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3

hypertension

类别分布:

14736	53.8%		1
12667	46.2%		0

elevated_BMI

← BMXBMI

> 25 kg/m²

类别分布:

20224	70.7%		1
8382	29.3%		0

prediabetes

← LBXGLU, LBXGH

glycated hemoglobin of 5.7% to < 6.5% or fasting blood glucose of 100 mg/dL to < 126 mg/dL

类别分布:

14574	50.3%		0
14397	49.7%		1

elevated_wc

← BMXWAIST, RIAGENDR

≥ 88 cm for women and ≥ 102 cm for men

类别分布:

16027	57.3%		1
11956	42.7%		0

low_hdl

← RIAGENDR, LBDHDL, LBXHDD, LBDHDD

< 40 mg/dL or < 50 mg/dL for men or women, respectively

类别分布:

20449	71.5%		0
8148	28.5%		1

high_tg

← LBXTR, LBXTLG

fasting serum triglycerides ≥ 150 mg/dL

类别分布:

21067	73.7%		0
7506	26.3%		1

elevated_bp

← BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3

systolic blood pressure ≥ 130mmHg, diastolic blood pressure ≥ 80 mmHg, or use of blood pressure-lowering medications

类别分布:

14108	51.7%		1
13182	48.3%		0

mets_count

← BMXWAIST, RIAGENDR, LBDHDL, LBXHDD, LBDHDD, LBXTR, LBXTLG, BPQ050A, BPQ150, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, LBXGLU, LBXGH

Count of metabolic syndrome components (0-5 points, based on IDF criteria: elevated waist circumference, low HDL-C, high triglycerides, elevated blood pressure, and prediabetes

类别分布:

7738	26.7%	2
6657	22.9%	3
6335	21.8%	1
3949	13.6%	0
3346	11.5%	4
1004	3.5%	5

MetS

Metabolic syndrome status (1 = metabolic syndrome diagnosed, 0 = not diagnosed, NA = unable to determine due to missing data; diagnostic threshold: ≥3 abnormal components

类别分布:

15628	58.7%		0
11007	41.3%		1

CKD

← LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR, URXUMA, URXUCR

KDIGO CKD stages:very high risk, moderate to high risk, low risk

类别分布:

23396	83%	Low
4208	14.9%	Moderate to High
568	2%	Very High

Cursmk

← SMQ040, SMQ020

current smoker

类别分布:

23481	81%		0
5520	19%		1

CVD_10risk

← RIDAGEYR, LBXTC, LBDHDL, LBXHDD, LBDHDD, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, LBXSCR, LBDSCR, RIAGENDR, DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU, SMQ040, SMQ020, BPQ050A, BPQ150

10-year cardiovascular disease risk

范围:
0.08 (0 ~ 0.72)

Target Definition

CKM_stage

← WTSAF4YR, WTSAFPRP, WTSAF2YR, MCQ160C, MCQ160B, MCQ160E, MCQ160F, MCQ160D, LBXSCR, LBDSCR, RIDAGEYR, RIAGENDR, URXUMA, URXUCR, LBXTC, LBDHDL, LBXHDD, LBDHDD, BPXSY1, BPXSY2, BPXSY3, BPXSY4, BPXOSY1, BPXOSY2, BPXOSY3, DIQ010, DIQ070, DID070, DIQ050, LBXGH, LBXGLU, SMQ040, SMQ020, BPQ050A, BPQ150, LBXTR, LBXTLG, BPQ020, BPQ030, BPXDI1, BPXDI2, BPXDI3, BPXDI4, BPXODI1, BPXODI2, BPXODI3, BMXWAIST, BMXBMI

CKM stages 0-4

类别分布:

14945	52.2%	2
5735	20%	1
3264	11.4%	4
2368	8.3%	0
2309	8.1%	3

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