NHANES EAA and fatty acid intake: Analyses

Libraries

library(tidyverse)
library(survey) 
library(forcats)
library(jtools)
library(dplyr)
library(mice)
library(mitools)
library(svydiags)
library(kableExtra)

Survey design

load("nhanes_fattyAcids_clean_data.RData")

# specify survey design
nhanesDesign <- svydesign(id = ~SDMVPSU,  # Primary Sampling Units (PSU)
                 strata  = ~SDMVSTRA, # Stratification used in the survey
                 weights = ~WTDN4YR,   # Survey weights (using those from DNAm dataset, may need to update)
                 nest    = TRUE,      # Whether PSUs are nested within strata
                 data    = df)       # specify dataset
dim(nhanesDesign)
# 2532  288

# subset survey design
# participants with fatty acid and epigenetic age data
nhanesDesign_use <- subset(nhanesDesign, use_fat)
dim(nhanesDesign_use)
# 2220  288

# participants with complete covariate data
nhanesDesign_use_covar <- subset(nhanesDesign, use_fat_covar)
dim(nhanesDesign_use_covar)
# 1771  288

# male participants
filterMale = ifelse(df$RIAGENDR == 1, TRUE, FALSE)
nhanesDesign_male <- subset(nhanesDesign, filterMale & use_fat_covar)
dim(nhanesDesign_male)
# 952 288

# female participants
filterFemale = ifelse(df$RIAGENDR == 2, TRUE, FALSE)
nhanesDesign_female <- subset(nhanesDesign, filterFemale & use_fat_covar)
dim(nhanesDesign_female)
# 819 288

Primary models

Linear associations adjusted for age, age^2, gender, race/ethnicity, BMI, education, occupation, PIR, alcohol, smoking, and physical activity

# dataframe to store results
fat_res_alcSmkAct = data.frame(nutrient = rep(c('Total fat', 'Saturated fat', 'Monounsaturated fat', 'Polyunsaturated fat', 'P:S ratio', 'Omega-3', 'Omega-6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 7),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA)

# NHANES variable names
vars_fat = c('DRXTTFAT_log2', 'DRXTSFAT_log2', 'DRXTMFAT_log2', 'DRXTPFAT_log2', 'psatfatRatio', 'omega3_log2', 'omega6_log2')
vars_ead = c('HorvathAge', 'SkinBloodAge', 'HannumAge', 'LinAge', 'ZhangAge', 'VidalBraloAge', 'PhenoAge', 'GrimAge2Mort', 'HorvathTelo', 'YangCell_scale', 'DunedinPoAm_scale')

# functions to fill in table
popTable1 = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", vars_fat[j], " +
        RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
        BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
        factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
        design = nhanesDesign_use, 
        family = gaussian())
    tab[k,3] = summary(mod)$coef[2,1]
    tab[k,4] = summary(mod)$coef[2,4]
    tab[k,5] = confint(mod)[2,1]
    tab[k,6] = confint(mod)[2,2]
    tab[k,7] = nrow(mod$model)
    return(tab)
}

popTable2 = function(tab, vars_fat, vars_ead){
    k = 1
    for (j in 1:length(vars_fat)){
        for (i in 1:length(vars_ead)){
            tab = popTable1(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

fat_res_alcSmkAct = popTable2(fat_res_alcSmkAct, vars_fat, vars_ead)

fat_res_alcSmkAct$B_CI = paste0(round(fat_res_alcSmkAct$coef, 2), ' (', round(fat_res_alcSmkAct$CI_L, 2), ', ', round(fat_res_alcSmkAct$CI_H, 2), ')')

write.csv(fat_res_alcSmkAct, 'fat_res_alcSmkAct.csv')

nutrient	clock	coef	p	CI_L	CI_H	n	B_CI
Total fat	Horvath1	-0.5927376	0.1276207	-1.3969365	0.2114612	1771	-0.59 (-1.4, 0.21)
Total fat	Horvath2	-0.0690771	0.8054067	-0.6945757	0.5564215	1771	-0.07 (-0.69, 0.56)
Total fat	Hannum	-0.2008399	0.6109061	-1.0756920	0.6740122	1771	-0.2 (-1.08, 0.67)
Total fat	Lin	-0.6051004	0.2979557	-1.8585881	0.6483874	1771	-0.61 (-1.86, 0.65)
Total fat	Zhang	-0.0040567	0.9713125	-0.2561903	0.2480769	1771	0 (-0.26, 0.25)
Total fat	VidalBralo	0.1853779	0.6707236	-0.7834497	1.1542054	1771	0.19 (-0.78, 1.15)
Total fat	PhenoAge	-0.1097686	0.8222227	-1.1999568	0.9804197	1771	-0.11 (-1.2, 0.98)
Total fat	GrimAge2	0.4973133	0.0907373	-0.0991439	1.0937705	1771	0.5 (-0.1, 1.09)
Total fat	DNAmTL	0.0001375	0.9902050	-0.0249054	0.0251805	1771	0 (-0.02, 0.03)
Total fat	YangCell	-0.0858916	0.1793516	-0.2204882	0.0487050	1771	-0.09 (-0.22, 0.05)
Total fat	DunedinPoAm	0.0826809	0.2128551	-0.0581721	0.2235339	1771	0.08 (-0.06, 0.22)
Saturated fat	Horvath1	-0.0070032	0.9725662	-0.4621737	0.4481672	1771	-0.01 (-0.46, 0.45)
Saturated fat	Horvath2	0.2107762	0.2346502	-0.1673893	0.5889418	1771	0.21 (-0.17, 0.59)
Saturated fat	Hannum	0.2273907	0.3797137	-0.3366095	0.7913910	1771	0.23 (-0.34, 0.79)
Saturated fat	Lin	-0.2411006	0.5299515	-1.0880304	0.6058293	1771	-0.24 (-1.09, 0.61)
Saturated fat	Zhang	0.0758893	0.2522664	-0.0659410	0.2177196	1771	0.08 (-0.07, 0.22)
Saturated fat	VidalBralo	0.3753992	0.2348844	-0.2984951	1.0492934	1771	0.38 (-0.3, 1.05)
Saturated fat	PhenoAge	0.3439826	0.3424277	-0.4420167	1.1299819	1771	0.34 (-0.44, 1.13)
Saturated fat	GrimAge2	0.4233781	0.0431543	0.0166379	0.8301183	1771	0.42 (0.02, 0.83)
Saturated fat	DNAmTL	-0.0000836	0.9913656	-0.0173478	0.0171807	1771	0 (-0.02, 0.02)
Saturated fat	YangCell	-0.0777473	0.1025278	-0.1750116	0.0195170	1771	-0.08 (-0.18, 0.02)
Saturated fat	DunedinPoAm	0.0669511	0.1929565	-0.0416581	0.1755602	1771	0.07 (-0.04, 0.18)
Monounsaturated fat	Horvath1	-0.4989153	0.1387613	-1.1986578	0.2008272	1771	-0.5 (-1.2, 0.2)
Monounsaturated fat	Horvath2	-0.0304127	0.8949249	-0.5448206	0.4839953	1771	-0.03 (-0.54, 0.48)
Monounsaturated fat	Hannum	0.0831411	0.8179608	-0.7228603	0.8891426	1771	0.08 (-0.72, 0.89)
Monounsaturated fat	Lin	-0.4363066	0.3859256	-1.5334338	0.6608205	1771	-0.44 (-1.53, 0.66)
Monounsaturated fat	Zhang	-0.0087022	0.9293371	-0.2279927	0.2105883	1771	-0.01 (-0.23, 0.21)
Monounsaturated fat	VidalBralo	0.4177659	0.2948271	-0.4416555	1.2771872	1771	0.42 (-0.44, 1.28)
Monounsaturated fat	PhenoAge	0.3186015	0.4588403	-0.6254825	1.2626855	1771	0.32 (-0.63, 1.26)
Monounsaturated fat	GrimAge2	0.5364348	0.0386394	0.0358556	1.0370141	1771	0.54 (0.04, 1.04)
Monounsaturated fat	DNAmTL	-0.0103149	0.3205352	-0.0327765	0.0121467	1771	-0.01 (-0.03, 0.01)
Monounsaturated fat	YangCell	-0.0578422	0.3118347	-0.1814039	0.0657196	1771	-0.06 (-0.18, 0.07)
Monounsaturated fat	DunedinPoAm	0.1054837	0.0776187	-0.0147182	0.2256856	1771	0.11 (-0.01, 0.23)
Polyunsaturated fat	Horvath1	-0.6752776	0.0287686	-1.2605067	-0.0900485	1771	-0.68 (-1.26, -0.09)
Polyunsaturated fat	Horvath2	-0.3399979	0.1382893	-0.8161982	0.1362025	1771	-0.34 (-0.82, 0.14)
Polyunsaturated fat	Hannum	-0.6040253	0.0342146	-1.1504155	-0.0576352	1771	-0.6 (-1.15, -0.06)
Polyunsaturated fat	Lin	-0.6392844	0.0661129	-1.3324074	0.0538387	1771	-0.64 (-1.33, 0.05)
Polyunsaturated fat	Zhang	-0.0873842	0.2754963	-0.2595448	0.0847763	1771	-0.09 (-0.26, 0.08)
Polyunsaturated fat	VidalBralo	-0.2714021	0.2779681	-0.8090858	0.2662816	1771	-0.27 (-0.81, 0.27)
Polyunsaturated fat	PhenoAge	-0.5875757	0.0404630	-1.1425186	-0.0326328	1771	-0.59 (-1.14, -0.03)
Polyunsaturated fat	GrimAge2	-0.0842849	0.6232946	-0.4647994	0.2962296	1771	-0.08 (-0.46, 0.3)
Polyunsaturated fat	DNAmTL	0.0029511	0.7110063	-0.0147724	0.0206746	1771	0 (-0.01, 0.02)
Polyunsaturated fat	YangCell	-0.0390977	0.3034690	-0.1210865	0.0428911	1771	-0.04 (-0.12, 0.04)
Polyunsaturated fat	DunedinPoAm	0.0049457	0.9021803	-0.0849538	0.0948452	1771	0 (-0.08, 0.09)
P:S ratio	Horvath1	-0.8701838	0.0265282	-1.6097830	-0.1305847	1771	-0.87 (-1.61, -0.13)
P:S ratio	Horvath2	-0.5391536	0.0935063	-1.1924177	0.1141105	1771	-0.54 (-1.19, 0.11)
P:S ratio	Hannum	-0.8865835	0.0231175	-1.6161629	-0.1570041	1771	-0.89 (-1.62, -0.16)
P:S ratio	Lin	-0.4239107	0.3175221	-1.3409009	0.4930795	1771	-0.42 (-1.34, 0.49)
P:S ratio	Zhang	-0.1589920	0.1061772	-0.3603896	0.0424055	1771	-0.16 (-0.36, 0.04)
P:S ratio	VidalBralo	-0.7994666	0.0409269	-1.5567991	-0.0421341	1771	-0.8 (-1.56, -0.04)
P:S ratio	PhenoAge	-1.0463152	0.0190801	-1.8705898	-0.2220406	1771	-1.05 (-1.87, -0.22)
P:S ratio	GrimAge2	-0.4885678	0.0741665	-1.0373555	0.0602200	1771	-0.49 (-1.04, 0.06)
P:S ratio	DNAmTL	0.0047600	0.6825422	-0.0211110	0.0306310	1771	0 (-0.02, 0.03)
P:S ratio	YangCell	0.0342950	0.5228158	-0.0840601	0.1526500	1771	0.03 (-0.08, 0.15)
P:S ratio	DunedinPoAm	-0.0544715	0.2392376	-0.1532543	0.0443112	1771	-0.05 (-0.15, 0.04)
Omega-3	Horvath1	-0.6300049	0.0142462	-1.0957653	-0.1642445	1771	-0.63 (-1.1, -0.16)
Omega-3	Horvath2	-0.2681881	0.0850375	-0.5829650	0.0465888	1771	-0.27 (-0.58, 0.05)
Omega-3	Hannum	-0.6694762	0.0199939	-1.2024400	-0.1365124	1771	-0.67 (-1.2, -0.14)
Omega-3	Lin	-0.5835604	0.0583696	-1.1933481	0.0262272	1771	-0.58 (-1.19, 0.03)
Omega-3	Zhang	-0.0704571	0.2474404	-0.2006812	0.0597670	1771	-0.07 (-0.2, 0.06)
Omega-3	VidalBralo	-0.4522497	0.0405446	-0.8796083	-0.0248910	1771	-0.45 (-0.88, -0.02)
Omega-3	PhenoAge	-0.7716650	0.0129295	-1.3306317	-0.2126983	1771	-0.77 (-1.33, -0.21)
Omega-3	GrimAge2	-0.1935607	0.3040805	-0.6000122	0.2128908	1771	-0.19 (-0.6, 0.21)
Omega-3	DNAmTL	0.0142466	0.1191358	-0.0045802	0.0330734	1771	0.01 (0, 0.03)
Omega-3	YangCell	-0.0395877	0.2789410	-0.1181879	0.0390124	1771	-0.04 (-0.12, 0.04)
Omega-3	DunedinPoAm	0.0082515	0.8325346	-0.0788454	0.0953483	1771	0.01 (-0.08, 0.1)
Omega-6	Horvath1	-0.6430553	0.0355525	-1.2304283	-0.0556824	1771	-0.64 (-1.23, -0.06)
Omega-6	Horvath2	-0.3257373	0.1562652	-0.8057738	0.1542993	1771	-0.33 (-0.81, 0.15)
Omega-6	Hannum	-0.5652276	0.0399946	-1.0974361	-0.0330191	1771	-0.57 (-1.1, -0.03)
Omega-6	Lin	-0.6314934	0.0659460	-1.3156472	0.0526604	1771	-0.63 (-1.32, 0.05)
Omega-6	Zhang	-0.0780759	0.3186329	-0.2473813	0.0912295	1771	-0.08 (-0.25, 0.09)
Omega-6	VidalBralo	-0.2509056	0.3019549	-0.7752982	0.2734869	1771	-0.25 (-0.78, 0.27)
Omega-6	PhenoAge	-0.5351880	0.0533827	-1.0802898	0.0099138	1771	-0.54 (-1.08, 0.01)
Omega-6	GrimAge2	-0.0745581	0.6606126	-0.4517195	0.3026032	1771	-0.07 (-0.45, 0.3)
Omega-6	DNAmTL	0.0014693	0.8482241	-0.0156703	0.0186089	1771	0 (-0.02, 0.02)
Omega-6	YangCell	-0.0348857	0.3333700	-0.1130284	0.0432570	1771	-0.03 (-0.11, 0.04)
Omega-6	DunedinPoAm	0.0032034	0.9359134	-0.0858295	0.0922364	1771	0 (-0.09, 0.09)

Omega-3 and omega-6 jointly adjusted model

# dataframe to store results
fat_res_alcSmkAct_omegas = data.frame(nutrient = rep(c('Omega-3', 'Omega-6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 2),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA, se = NA)

# NHANES variable names
vars_ead = c('HorvathAge', 'SkinBloodAge', 'HannumAge', 'LinAge', 'ZhangAge', 'VidalBraloAge', 'PhenoAge', 'GrimAge2Mort', 'HorvathTelo', 'YangCell_scale', 'DunedinPoAm_scale')

# functions to fill in table
popTable_omegas = function(tab, vars_ead){
    for (i in 1:length(vars_ead)){
        mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
        tab[i,3] = summary(mod)$coef[2,1]
        tab[i,4] = summary(mod)$coef[2,4]
        tab[i,5] = confint(mod)[2,1]
        tab[i,6] = confint(mod)[2,2]
        tab[i,7] = nrow(mod$model)
        tab[i,8] = summary(mod)$coef[2,2]
        tab[i+11,3] = summary(mod)$coef[3,1]
        tab[i+11,4] = summary(mod)$coef[3,4]
        tab[i+11,5] = confint(mod)[3,1]
        tab[i+11,6] = confint(mod)[3,2]
        tab[i+11,7] = nrow(mod$model)   
        tab[i+11,8] = summary(mod)$coef[3,2]
    }
    return(tab)
}

fat_res_alcSmkAct_omegas = popTable_omegas(fat_res_alcSmkAct_omegas, vars_ead)

fat_res_alcSmkAct_omegas$B_CI = paste0(round(fat_res_alcSmkAct_omegas$coef, 2), ' (', round(fat_res_alcSmkAct_omegas$CI_L, 2), ', ', round(fat_res_alcSmkAct_omegas$CI_H, 2), ')')

write.csv(fat_res_alcSmkAct_omegas, 'fat_res_alcSmkAct_omegas.csv')

# calculate VIF
X = model.matrix(formula(" ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR"), data = df[df$use_fat_covar,])

# Horvath1
i = 1
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
# $`Intercept adjusted`
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.967946   3.212232 1309589.2 7.055276e-07       0.9239513 0.68868994
# omega6_log2                              3.597126   3.694117 1576102.2 6.178180e-07       0.9737444 0.72929935

# Horvath2
i = 2
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.530812   3.212232  725017.5 1.086687e-06       0.7878674 0.68868994
# omega6_log2                              3.167558   3.694117  996926.8 8.601034e-07       0.8574601 0.72929935

# Hannum
i = 3
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.623826   3.212232 1136967.8 7.184226e-07       0.8168234 0.68868994
# omega6_log2                              3.163919   3.694117 1575851.7 5.434998e-07       0.8564750 0.72929935

# Lin
i = 4
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.976786   3.212232 2997642.8 3.091440e-07       0.9267034 0.68868994
# omega6_log2                              3.291811   3.694117 3094102.4 2.879981e-07       0.8910955 0.72929935

# Zhang
i = 5
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                          svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.2000084   3.212232  86930.61 7.878522e-06       0.6848847 0.68868994
# omega6_log2                              2.8517601   3.694117 135412.20 5.700914e-06       0.7719733 0.72929935

# Vidal-Bralo
i = 6
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              3.151974   3.212232 1257285.5 7.804443e-07       0.9812413 0.68868994
# omega6_log2                              3.702299   3.694117 1360590.3 7.366028e-07       1.0022147 0.72929935

# PhenoAge
i = 7
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.961911   3.212232 2113404.1 4.362974e-07       0.9220726 0.68868994
# omega6_log2                              3.514716   3.694117 2519250.1 3.776663e-07       0.9514360 0.72929935

# GrimAge2
i = 8
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              3.175466   3.212232  854816.1 1.156453e-06       0.9885546 0.68868994
# omega6_log2                              3.717093   3.694117  788996.5 1.275316e-06       1.0062196 0.72929935

# DNAmTL
i = 9
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.829337   3.212232 2170.4203 0.0004058205       0.8808010 0.68868994
# omega6_log2                              3.487489   3.694117 2832.5737 0.0003332890       0.9440657 0.72929935

# YangCell
i = 10
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              3.449913   3.212232 34265.484 3.134328e-05       1.0739926 0.68868994
# omega6_log2                              3.497886   3.694117 33411.856 2.833964e-05       0.9468801 0.72929935

# DunedinPoAm
i = 11
mod = svyglm(formula(paste0(vars_ead[i], " ~ omega3_log2 + omega6_log2 +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
svyvif(mod, X = data.frame(X[,-1]), w = data.frame(df[df$use_fat_covar,])$WTDN4YR)
#                                         svy.vif.m  reg.vif.m      zeta     varrho.m zeta.x.varrho.m      Rsq.m
# omega3_log2                              2.929468   3.212232 34781.148 2.622032e-05       0.9119727 0.68868994
# omega6_log2                              3.428352   3.694117 35402.935 2.621413e-05       0.9280572 0.72929935

nutrient	clock	coef	p	CI_L	CI_H	n	se	B_CI
Omega-3	Horvath1	-0.4352649	0.2053140	-1.1723998	0.3018700	1771	0.3117345	-0.44 (-1.17, 0.3)
Omega-3	Horvath2	-0.1219039	0.5873857	-0.6288469	0.3850391	1771	0.2143863	-0.12 (-0.63, 0.39)
Omega-3	Hannum	-0.6065301	0.1100800	-1.3906122	0.1775520	1771	0.3315885	-0.61 (-1.39, 0.18)
Omega-3	Lin	-0.3594790	0.4167219	-1.3444519	0.6254939	1771	0.4165452	-0.36 (-1.34, 0.63)
Omega-3	Zhang	-0.0411695	0.5879192	-0.2126252	0.1302862	1771	0.0725086	-0.04 (-0.21, 0.13)
Omega-3	VidalBralo	-0.5693325	0.0468111	-1.1280708	-0.0105941	1771	0.2362905	-0.57 (-1.13, -0.01)
Omega-3	PhenoAge	-0.8409103	0.0409114	-1.6358562	-0.0459644	1771	0.3361828	-0.84 (-1.64, -0.05)
Omega-3	GrimAge2	-0.2836922	0.3323209	-0.9278562	0.3604719	1771	0.2724171	-0.28 (-0.93, 0.36)
Omega-3	DNAmTL	0.0257793	0.0781148	-0.0037810	0.0553397	1771	0.0125011	0.03 (0, 0.06)
Omega-3	YangCell	-0.0340828	0.4990428	-0.1471503	0.0789848	1771	0.0478163	-0.03 (-0.15, 0.08)
Omega-3	DunedinPoAm	0.0120632	0.8278853	-0.1143232	0.1384496	1771	0.0534488	0.01 (-0.11, 0.14)
Omega-6	Horvath1	-0.3091385	0.4575774	-1.2390158	0.6207388	1771	0.3932453	-0.31 (-1.24, 0.62)
Omega-6	Horvath2	-0.2322178	0.4793285	-0.9671819	0.5027464	1771	0.3108165	-0.23 (-0.97, 0.5)
Omega-6	Hannum	-0.0999233	0.7777710	-0.9054195	0.7055728	1771	0.3406445	-0.1 (-0.91, 0.71)
Omega-6	Lin	-0.3557163	0.4720702	-1.4624734	0.7510408	1771	0.4680478	-0.36 (-1.46, 0.75)
Omega-6	Zhang	-0.0464924	0.6468080	-0.2761959	0.1832111	1771	0.0971416	-0.05 (-0.28, 0.18)
Omega-6	VidalBralo	0.1858622	0.5589598	-0.5305364	0.9022609	1771	0.3029651	0.19 (-0.53, 0.9)
Omega-6	PhenoAge	0.1099229	0.7492012	-0.6716614	0.8915073	1771	0.3305322	0.11 (-0.67, 0.89)
Omega-6	GrimAge2	0.1430785	0.5945441	-0.4637655	0.7499225	1771	0.2566344	0.14 (-0.46, 0.75)
Omega-6	DNAmTL	-0.0183075	0.1675965	-0.0464289	0.0098138	1771	0.0118925	-0.02 (-0.05, 0.01)
Omega-6	YangCell	-0.0087388	0.8590873	-0.1209280	0.1034504	1771	0.0474448	-0.01 (-0.12, 0.1)
Omega-6	DunedinPoAm	-0.0060510	0.9151260	-0.1355556	0.1234537	1771	0.0547675	-0.01 (-0.14, 0.12)

Associations with individual fatty acids

# dataframe to store results
subtypes_res = data.frame(nutrient = rep(c('SFA 4:0', 'SFA 6:0', 'SFA 8:0', 'SFA 10:0', 'SFA 12:0', 'SFA 14:0', 'SFA 16:0', 'SFA 18:0',
                            'MUFA 16:1', 'MUFA 18:1', 'MUFA 20:1', 'MUFA 22:1',
                            'PUFA 18:2', 'PUFA 20:4', 'PUFA 18:3', 'PUFA 20:5', 'PUFA 22:5', 'PUFA 22:6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 18),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA)

# NHANES variable names
vars_ead = c("HorvathAge", "SkinBloodAge", "HannumAge", "LinAge", "ZhangAge", "VidalBraloAge", "PhenoAge", "GrimAge2Mort", "HorvathTelo", "YangCell_scale", "DunedinPoAm_scale")
subtypes = c("DRXTS040_log2", "DRXTS060_log2", "DRXTS080_log2", "DRXTS100_log2", "DRXTS120_log2", "DRXTS140_log2", "DRXTS160_log2", "DRXTS180_log2", "DRXTM161_log2", 
        "DRXTM181_log2", "DRXTM201_log2", "DRXTM221_log2", "DRXTP182_log2", "DRXTP204_log2", "DRXTP183_log2", "DRXTP205_log2", "DRXTP225_log2", "DRXTP226_log2")

# functions to fill in table
popTable1_subtypes = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", subtypes[j], " +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
    tab[k,3] = summary(mod)$coef[2,1]
    tab[k,4] = summary(mod)$coef[2,4]
    tab[k,5] = confint(mod)[2,1]
    tab[k,6] = confint(mod)[2,2]
    tab[k,7] = nrow(mod$model)
    return(tab)
}

popTable2_subtypes = function(tab, subtypes, vars_ead){
    k = 1
    for (j in 1:length(subtypes)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_subtypes(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

subtypes_res = popTable2_subtypes(subtypes_res, subtypes, vars_ead)

subtypes_res$B_CI = paste0(round(subtypes_res$coef, 2), ' (', round(subtypes_res$CI_L, 2), ', ', round(subtypes_res$CI_H, 2), ')')

write.csv(subtypes_res, 'subtypes_res_noScale.csv')


# results for plotting
# dataframes to store results
type_B = data.frame(matrix(nrow = 18, ncol = 11))
rownames(type_B) = subtypes
colnames(type_B) = c("HorvathAge", "SkinBloodAge", "HannumAge", "LinAge", "ZhangAge", "VidalBraloAge", "PhenoAge", "GrimAge2Mort", "HorvathTelo", "YangCell_scale", "DunedinPoAm")
type_p = type_B

for (i in 1:nrow(type_B)){
    for (j in 1:ncol(type_B)){
        coef = summary(svyglm(formula(paste0("scale(", colnames(type_B)[j], ") ~ ", rownames(type_B)[i], " +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian()))$coef
        type_B[i,j] = coef[2,1]
        type_p[i,j] = coef[2,4]
    }
}

write.csv(type_B, 'subtypes_B.csv')
write.csv(type_p, 'subtypes_p.csv')

nutrient	clock	coef	p	CI_L	CI_H	n	B_CI
SFA 4:0	Horvath1	0.0297385	0.4322779	-0.0531969	0.1126739	1771	0.03 (-0.05, 0.11)
SFA 4:0	Horvath2	0.0223400	0.3962706	-0.0351376	0.0798175	1771	0.02 (-0.04, 0.08)
SFA 4:0	Hannum	0.0359249	0.4512639	-0.0686891	0.1405389	1771	0.04 (-0.07, 0.14)
SFA 4:0	Lin	-0.0448376	0.6242103	-0.2478055	0.1581304	1771	-0.04 (-0.25, 0.16)
SFA 4:0	Zhang	0.0073637	0.5528665	-0.0200501	0.0347775	1771	0.01 (-0.02, 0.03)
SFA 4:0	VidalBralo	0.0602672	0.3237886	-0.0719134	0.1924477	1771	0.06 (-0.07, 0.19)
SFA 4:0	PhenoAge	0.0346369	0.6185231	-0.1195792	0.1888530	1771	0.03 (-0.12, 0.19)
SFA 4:0	GrimAge2	0.0427403	0.4818876	-0.0908997	0.1763802	1771	0.04 (-0.09, 0.18)
SFA 4:0	DNAmTL	-0.0009186	0.4696625	-0.0037100	0.0018727	1771	0 (0, 0)
SFA 4:0	YangCell	-0.0057330	0.4283945	-0.0215816	0.0101156	1771	-0.01 (-0.02, 0.01)
SFA 4:0	DunedinPoAm	0.0078568	0.2964851	-0.0083658	0.0240793	1771	0.01 (-0.01, 0.02)
SFA 6:0	Horvath1	0.0205991	0.6514969	-0.0806557	0.1218539	1771	0.02 (-0.08, 0.12)
SFA 6:0	Horvath2	0.0232873	0.4459265	-0.0437026	0.0902772	1771	0.02 (-0.04, 0.09)
SFA 6:0	Hannum	0.0474360	0.3102280	-0.0535371	0.1484091	1771	0.05 (-0.05, 0.15)
SFA 6:0	Lin	-0.0463173	0.5275470	-0.2080481	0.1154134	1771	-0.05 (-0.21, 0.12)
SFA 6:0	Zhang	0.0039764	0.7809498	-0.0279048	0.0358576	1771	0 (-0.03, 0.04)
SFA 6:0	VidalBralo	0.0771051	0.1571190	-0.0367906	0.1910008	1771	0.08 (-0.04, 0.19)
SFA 6:0	PhenoAge	0.0491760	0.4417892	-0.0909590	0.1893109	1771	0.05 (-0.09, 0.19)
SFA 6:0	GrimAge2	0.0557368	0.3310915	-0.0684877	0.1799612	1771	0.06 (-0.07, 0.18)
SFA 6:0	DNAmTL	-0.0008366	0.5444517	-0.0038845	0.0022114	1771	0 (0, 0)
SFA 6:0	YangCell	-0.0038863	0.5755422	-0.0192411	0.0114686	1771	0 (-0.02, 0.01)
SFA 6:0	DunedinPoAm	0.0078188	0.3347958	-0.0097500	0.0253876	1771	0.01 (-0.01, 0.03)
SFA 8:0	Horvath1	0.0368085	0.4665111	-0.0742170	0.1478339	1771	0.04 (-0.07, 0.15)
SFA 8:0	Horvath2	0.0187833	0.6481139	-0.0725824	0.1101491	1771	0.02 (-0.07, 0.11)
SFA 8:0	Hannum	-0.0023016	0.9673247	-0.1278818	0.1232787	1771	0 (-0.13, 0.12)
SFA 8:0	Lin	-0.0326811	0.7555385	-0.2665606	0.2011984	1771	-0.03 (-0.27, 0.2)
SFA 8:0	Zhang	0.0002075	0.9903984	-0.0383332	0.0387482	1771	0 (-0.04, 0.04)
SFA 8:0	VidalBralo	0.0596437	0.3515798	-0.0794098	0.1986971	1771	0.06 (-0.08, 0.2)
SFA 8:0	PhenoAge	-0.0445380	0.4975865	-0.1890927	0.1000168	1771	-0.04 (-0.19, 0.1)
SFA 8:0	GrimAge2	0.0367498	0.4997960	-0.0831615	0.1566612	1771	0.04 (-0.08, 0.16)
SFA 8:0	DNAmTL	0.0004233	0.8391116	-0.0042305	0.0050771	1771	0 (0, 0.01)
SFA 8:0	YangCell	-0.0132043	0.1480993	-0.0322261	0.0058175	1771	-0.01 (-0.03, 0.01)
SFA 8:0	DunedinPoAm	0.0049024	0.6343330	-0.0179650	0.0277697	1771	0 (-0.02, 0.03)
SFA 10:0	Horvath1	0.0464418	0.4889637	-0.1012234	0.1941071	1771	0.05 (-0.1, 0.19)
SFA 10:0	Horvath2	0.0267314	0.6309051	-0.0966892	0.1501520	1771	0.03 (-0.1, 0.15)
SFA 10:0	Hannum	0.0031988	0.9640134	-0.1552652	0.1616627	1771	0 (-0.16, 0.16)
SFA 10:0	Lin	-0.1048645	0.3924503	-0.3723898	0.1626609	1771	-0.1 (-0.37, 0.16)
SFA 10:0	Zhang	0.0022711	0.9095548	-0.0423962	0.0469384	1771	0 (-0.04, 0.05)
SFA 10:0	VidalBralo	0.0448486	0.5584264	-0.1245355	0.2142327	1771	0.04 (-0.12, 0.21)
SFA 10:0	PhenoAge	-0.0284969	0.7091380	-0.1984798	0.1414860	1771	-0.03 (-0.2, 0.14)
SFA 10:0	GrimAge2	0.0557061	0.3895036	-0.0854843	0.1968966	1771	0.06 (-0.09, 0.2)
SFA 10:0	DNAmTL	0.0023069	0.4236723	-0.0040030	0.0086168	1771	0 (0, 0.01)
SFA 10:0	YangCell	-0.0175163	0.1106889	-0.0400412	0.0050085	1771	-0.02 (-0.04, 0.01)
SFA 10:0	DunedinPoAm	0.0116251	0.4141584	-0.0195006	0.0427507	1771	0.01 (-0.02, 0.04)
SFA 12:0	Horvath1	0.0465290	0.4928802	-0.1027984	0.1958564	1771	0.05 (-0.1, 0.2)
SFA 12:0	Horvath2	0.0325383	0.6275149	-0.1161923	0.1812688	1771	0.03 (-0.12, 0.18)
SFA 12:0	Hannum	-0.0441848	0.6043494	-0.2331099	0.1447402	1771	-0.04 (-0.23, 0.14)
SFA 12:0	Lin	-0.1178173	0.4390757	-0.4514912	0.2158566	1771	-0.12 (-0.45, 0.22)
SFA 12:0	Zhang	0.0014791	0.9441699	-0.0457236	0.0486818	1771	0 (-0.05, 0.05)
SFA 12:0	VidalBralo	0.0314291	0.7628446	-0.2006889	0.2635471	1771	0.03 (-0.2, 0.26)
SFA 12:0	PhenoAge	-0.1042404	0.2787714	-0.3111273	0.1026465	1771	-0.1 (-0.31, 0.1)
SFA 12:0	GrimAge2	0.0789874	0.2903368	-0.0818878	0.2398625	1771	0.08 (-0.08, 0.24)
SFA 12:0	DNAmTL	0.0032844	0.3558747	-0.0044455	0.0110143	1771	0 (0, 0.01)
SFA 12:0	YangCell	-0.0311406	0.0450922	-0.0614125	-0.0008688	1771	-0.03 (-0.06, 0)
SFA 12:0	DunedinPoAm	0.0128573	0.5016448	-0.0292819	0.0549966	1771	0.01 (-0.03, 0.05)
SFA 14:0	Horvath1	0.0891323	0.3794909	-0.1318350	0.3100996	1771	0.09 (-0.13, 0.31)
SFA 14:0	Horvath2	0.0851666	0.4239353	-0.1479201	0.3182533	1771	0.09 (-0.15, 0.32)
SFA 14:0	Hannum	0.0695022	0.5882379	-0.2146886	0.3536930	1771	0.07 (-0.21, 0.35)
SFA 14:0	Lin	-0.1482217	0.4620936	-0.5907483	0.2943048	1771	-0.15 (-0.59, 0.29)
SFA 14:0	Zhang	0.0205743	0.5589626	-0.0572390	0.0983875	1771	0.02 (-0.06, 0.1)
SFA 14:0	VidalBralo	0.1121995	0.4551669	-0.2174697	0.4418688	1771	0.11 (-0.22, 0.44)
SFA 14:0	PhenoAge	0.0274511	0.8427952	-0.2815474	0.3364497	1771	0.03 (-0.28, 0.34)
SFA 14:0	GrimAge2	0.1225061	0.2375392	-0.0987781	0.3437902	1771	0.12 (-0.1, 0.34)
SFA 14:0	DNAmTL	0.0071140	0.1620122	-0.0035353	0.0177634	1771	0.01 (0, 0.02)
SFA 14:0	YangCell	-0.0446578	0.0556219	-0.0906780	0.0013625	1771	-0.04 (-0.09, 0)
SFA 14:0	DunedinPoAm	0.0233574	0.3781878	-0.0343815	0.0810962	1771	0.02 (-0.03, 0.08)
SFA 16:0	Horvath1	-0.0630681	0.8053976	-0.6341277	0.5079915	1771	-0.06 (-0.63, 0.51)
SFA 16:0	Horvath2	0.2598166	0.1756943	-0.1434860	0.6631192	1771	0.26 (-0.14, 0.66)
SFA 16:0	Hannum	0.3776931	0.2347410	-0.3000915	1.0554778	1771	0.38 (-0.3, 1.06)
SFA 16:0	Lin	-0.1421332	0.7570404	-1.1658323	0.8815659	1771	-0.14 (-1.17, 0.88)
SFA 16:0	Zhang	0.1181133	0.1298422	-0.0432203	0.2794469	1771	0.12 (-0.04, 0.28)
SFA 16:0	VidalBralo	0.4457353	0.2179406	-0.3229573	1.2144278	1771	0.45 (-0.32, 1.21)
SFA 16:0	PhenoAge	0.5816773	0.1929430	-0.3618974	1.5252519	1771	0.58 (-0.36, 1.53)
SFA 16:0	GrimAge2	0.5541727	0.0305723	0.0667210	1.0416244	1771	0.55 (0.07, 1.04)
SFA 16:0	DNAmTL	-0.0074816	0.4089099	-0.0272796	0.0123164	1771	-0.01 (-0.03, 0.01)
SFA 16:0	YangCell	-0.0665457	0.2220729	-0.1824459	0.0493544	1771	-0.07 (-0.18, 0.05)
SFA 16:0	DunedinPoAm	0.0969452	0.1183140	-0.0308341	0.2247244	1771	0.1 (-0.03, 0.22)
SFA 18:0	Horvath1	-0.0665514	0.7546946	-0.5411168	0.4080141	1771	-0.07 (-0.54, 0.41)
SFA 18:0	Horvath2	0.2061573	0.1585145	-0.0995323	0.5118469	1771	0.21 (-0.1, 0.51)
SFA 18:0	Hannum	0.2387746	0.3340621	-0.2968864	0.7744355	1771	0.24 (-0.3, 0.77)
SFA 18:0	Lin	-0.2457998	0.4539132	-0.9659404	0.4743408	1771	-0.25 (-0.97, 0.47)
SFA 18:0	Zhang	0.0767835	0.2116965	-0.0536568	0.2072239	1771	0.08 (-0.05, 0.21)
SFA 18:0	VidalBralo	0.5205202	0.1210756	-0.1715589	1.2125993	1771	0.52 (-0.17, 1.21)
SFA 18:0	PhenoAge	0.4923817	0.1822731	-0.2850290	1.2697924	1771	0.49 (-0.29, 1.27)
SFA 18:0	GrimAge2	0.4293044	0.0285104	0.0580591	0.8005497	1771	0.43 (0.06, 0.8)
SFA 18:0	DNAmTL	-0.0054063	0.5002831	-0.0230673	0.0122547	1771	-0.01 (-0.02, 0.01)
SFA 18:0	YangCell	-0.0601625	0.1737260	-0.1530714	0.0327464	1771	-0.06 (-0.15, 0.03)
SFA 18:0	DunedinPoAm	0.0578414	0.2364053	-0.0463620	0.1620448	1771	0.06 (-0.05, 0.16)
MUFA 16:1	Horvath1	0.0619957	0.6826642	-0.2750973	0.3990887	1771	0.06 (-0.28, 0.4)
MUFA 16:1	Horvath2	0.2598290	0.0341632	0.0248815	0.4947766	1771	0.26 (0.02, 0.49)
MUFA 16:1	Hannum	0.4025442	0.0669328	-0.0355319	0.8406204	1771	0.4 (-0.04, 0.84)
MUFA 16:1	Lin	-0.0053243	0.9836593	-0.5863788	0.5757303	1771	-0.01 (-0.59, 0.58)
MUFA 16:1	Zhang	0.0731797	0.1051408	-0.0191931	0.1655525	1771	0.07 (-0.02, 0.17)
MUFA 16:1	VidalBralo	0.2426138	0.2357555	-0.1938005	0.6790282	1771	0.24 (-0.19, 0.68)
MUFA 16:1	PhenoAge	0.4807668	0.0674932	-0.0437627	1.0052963	1771	0.48 (-0.04, 1.01)
MUFA 16:1	GrimAge2	0.3007665	0.1228541	-0.1013570	0.7028899	1771	0.3 (-0.1, 0.7)
MUFA 16:1	DNAmTL	-0.0041953	0.5202975	-0.0185838	0.0101933	1771	0 (-0.02, 0.01)
MUFA 16:1	YangCell	-0.0351386	0.2970114	-0.1077772	0.0374999	1771	-0.04 (-0.11, 0.04)
MUFA 16:1	DunedinPoAm	0.0781109	0.0356596	0.0067090	0.1495128	1771	0.08 (0.01, 0.15)
MUFA 18:1	Horvath1	-0.5348068	0.1102445	-1.2215259	0.1519123	1771	-0.53 (-1.22, 0.15)
MUFA 18:1	Horvath2	-0.0611414	0.7931492	-0.5811425	0.4588597	1771	-0.06 (-0.58, 0.46)
MUFA 18:1	Hannum	0.0251784	0.9444735	-0.7827462	0.8331030	1771	0.03 (-0.78, 0.83)
MUFA 18:1	Lin	-0.4719116	0.3329481	-1.5279966	0.5841735	1771	-0.47 (-1.53, 0.58)
MUFA 18:1	Zhang	-0.0131299	0.8919908	-0.2291363	0.2028766	1771	-0.01 (-0.23, 0.2)
MUFA 18:1	VidalBralo	0.3550231	0.3500506	-0.4699028	1.1799490	1771	0.36 (-0.47, 1.18)
MUFA 18:1	PhenoAge	0.2384242	0.5677075	-0.6842085	1.1610569	1771	0.24 (-0.68, 1.16)
MUFA 18:1	GrimAge2	0.4873386	0.0541769	-0.0111108	0.9857879	1771	0.49 (-0.01, 0.99)
MUFA 18:1	DNAmTL	-0.0088843	0.3818393	-0.0310238	0.0132552	1771	-0.01 (-0.03, 0.01)
MUFA 18:1	YangCell	-0.0567769	0.3147152	-0.1788369	0.0652830	1771	-0.06 (-0.18, 0.07)
MUFA 18:1	DunedinPoAm	0.1005160	0.0975745	-0.0230708	0.2241028	1771	0.1 (-0.02, 0.22)
MUFA 20:1	Horvath1	-0.2027798	0.2188059	-0.5532093	0.1476496	1771	-0.2 (-0.55, 0.15)
MUFA 20:1	Horvath2	-0.0932375	0.3855473	-0.3274943	0.1410194	1771	-0.09 (-0.33, 0.14)
MUFA 20:1	Hannum	-0.1633015	0.3243945	-0.5219394	0.1953364	1771	-0.16 (-0.52, 0.2)
MUFA 20:1	Lin	-0.2569924	0.1116401	-0.5885041	0.0745193	1771	-0.26 (-0.59, 0.07)
MUFA 20:1	Zhang	-0.0598367	0.2573398	-0.1729725	0.0532992	1771	-0.06 (-0.17, 0.05)
MUFA 20:1	VidalBralo	-0.0129954	0.9035815	-0.2526699	0.2266791	1771	-0.01 (-0.25, 0.23)
MUFA 20:1	PhenoAge	-0.1388110	0.3609432	-0.4691641	0.1915420	1771	-0.14 (-0.47, 0.19)
MUFA 20:1	GrimAge2	0.1090061	0.1502300	-0.0489685	0.2669808	1771	0.11 (-0.05, 0.27)
MUFA 20:1	DNAmTL	-0.0041402	0.4165366	-0.0152845	0.0070041	1771	0 (-0.02, 0.01)
MUFA 20:1	YangCell	-0.0152728	0.4838534	-0.0632498	0.0327041	1771	-0.02 (-0.06, 0.03)
MUFA 20:1	DunedinPoAm	0.0192451	0.2375509	-0.0155185	0.0540086	1771	0.02 (-0.02, 0.05)
MUFA 22:1	Horvath1	0.0150883	0.7878637	-0.1099482	0.1401247	1771	0.02 (-0.11, 0.14)
MUFA 22:1	Horvath2	0.0221830	0.5849254	-0.0677073	0.1120733	1771	0.02 (-0.07, 0.11)
MUFA 22:1	Hannum	-0.0163687	0.7565907	-0.1340365	0.1012990	1771	-0.02 (-0.13, 0.1)
MUFA 22:1	Lin	-0.0345389	0.5826893	-0.1736521	0.1045743	1771	-0.03 (-0.17, 0.1)
MUFA 22:1	Zhang	-0.0047564	0.7579046	-0.0391411	0.0296282	1771	0 (-0.04, 0.03)
MUFA 22:1	VidalBralo	-0.0348845	0.3610153	-0.1179183	0.0481494	1771	-0.03 (-0.12, 0.05)
MUFA 22:1	PhenoAge	0.0447288	0.5008786	-0.1015976	0.1910552	1771	0.04 (-0.1, 0.19)
MUFA 22:1	GrimAge2	-0.0349630	0.3349128	-0.1135448	0.0436189	1771	-0.03 (-0.11, 0.04)
MUFA 22:1	DNAmTL	0.0003165	0.8581466	-0.0036369	0.0042698	1771	0 (0, 0)
MUFA 22:1	YangCell	0.0015109	0.8266734	-0.0138880	0.0169098	1771	0 (-0.01, 0.02)
MUFA 22:1	DunedinPoAm	0.0057601	0.4431886	-0.0107067	0.0222269	1771	0.01 (-0.01, 0.02)
PUFA 18:2	Horvath1	-0.6324011	0.0368450	-1.2153354	-0.0494669	1771	-0.63 (-1.22, -0.05)
PUFA 18:2	Horvath2	-0.3212689	0.1583084	-0.7973778	0.1548400	1771	-0.32 (-0.8, 0.15)
PUFA 18:2	Hannum	-0.5611960	0.0395604	-1.0881028	-0.0342892	1771	-0.56 (-1.09, -0.03)
PUFA 18:2	Lin	-0.6252856	0.0665805	-1.3046771	0.0541059	1771	-0.63 (-1.3, 0.05)
PUFA 18:2	Zhang	-0.0766272	0.3237181	-0.2446631	0.0914088	1771	-0.08 (-0.24, 0.09)
PUFA 18:2	VidalBralo	-0.2506147	0.2978876	-0.7696937	0.2684644	1771	-0.25 (-0.77, 0.27)
PUFA 18:2	PhenoAge	-0.5285708	0.0535754	-1.0674824	0.0103408	1771	-0.53 (-1.07, 0.01)
PUFA 18:2	GrimAge2	-0.0760758	0.6513987	-0.4499124	0.2977608	1771	-0.08 (-0.45, 0.3)
PUFA 18:2	DNAmTL	0.0013269	0.8615779	-0.0156662	0.0183200	1771	0 (-0.02, 0.02)
PUFA 18:2	YangCell	-0.0334600	0.3467832	-0.1106509	0.0437310	1771	-0.03 (-0.11, 0.04)
PUFA 18:2	DunedinPoAm	0.0017623	0.9643491	-0.0863646	0.0898893	1771	0 (-0.09, 0.09)
PUFA 20:4	Horvath1	-0.0764681	0.2776731	-0.2278613	0.0749251	1771	-0.08 (-0.23, 0.07)
PUFA 20:4	Horvath2	-0.0135902	0.7829440	-0.1235834	0.0964031	1771	-0.01 (-0.12, 0.1)
PUFA 20:4	Hannum	-0.0281585	0.7187557	-0.2021897	0.1458727	1771	-0.03 (-0.2, 0.15)
PUFA 20:4	Lin	-0.1510815	0.1174589	-0.3496744	0.0475113	1771	-0.15 (-0.35, 0.05)
PUFA 20:4	Zhang	-0.0265029	0.2342717	-0.0740111	0.0210053	1771	-0.03 (-0.07, 0.02)
PUFA 20:4	VidalBralo	-0.0952962	0.1444667	-0.2311696	0.0405772	1771	-0.1 (-0.23, 0.04)
PUFA 20:4	PhenoAge	-0.0550041	0.5464005	-0.2564073	0.1463991	1771	-0.06 (-0.26, 0.15)
PUFA 20:4	GrimAge2	0.0128518	0.7855289	-0.0924573	0.1181608	1771	0.01 (-0.09, 0.12)
PUFA 20:4	DNAmTL	0.0005586	0.8188296	-0.0048831	0.0060003	1771	0 (0, 0.01)
PUFA 20:4	YangCell	-0.0120364	0.3313888	-0.0388802	0.0148075	1771	-0.01 (-0.04, 0.01)
PUFA 20:4	DunedinPoAm	0.0192672	0.1890988	-0.0116865	0.0502210	1771	0.02 (-0.01, 0.05)
PUFA 18:3	Horvath1	-0.5700512	0.0242715	-1.0444996	-0.0956029	1771	-0.57 (-1.04, -0.1)
PUFA 18:3	Horvath2	-0.2210661	0.1740006	-0.5627041	0.1205718	1771	-0.22 (-0.56, 0.12)
PUFA 18:3	Hannum	-0.5974593	0.0328092	-1.1322543	-0.0626642	1771	-0.6 (-1.13, -0.06)
PUFA 18:3	Lin	-0.5558122	0.0720673	-1.1745622	0.0629377	1771	-0.56 (-1.17, 0.06)
PUFA 18:3	Zhang	-0.0337384	0.5903326	-0.1724864	0.1050096	1771	-0.03 (-0.17, 0.11)
PUFA 18:3	VidalBralo	-0.3629673	0.1071376	-0.8242341	0.0982994	1771	-0.36 (-0.82, 0.1)
PUFA 18:3	PhenoAge	-0.6868575	0.0355323	-1.3141490	-0.0595659	1771	-0.69 (-1.31, -0.06)
PUFA 18:3	GrimAge2	-0.1946743	0.3227206	-0.6206395	0.2312908	1771	-0.19 (-0.62, 0.23)
PUFA 18:3	DNAmTL	0.0081562	0.3839251	-0.0122628	0.0285752	1771	0.01 (-0.01, 0.03)
PUFA 18:3	YangCell	-0.0214603	0.5728041	-0.1056341	0.0627136	1771	-0.02 (-0.11, 0.06)
PUFA 18:3	DunedinPoAm	-0.0107485	0.7841665	-0.0982505	0.0767535	1771	-0.01 (-0.1, 0.08)
PUFA 20:5	Horvath1	-0.0787740	0.1699883	-0.1992345	0.0416865	1771	-0.08 (-0.2, 0.04)
PUFA 20:5	Horvath2	-0.0308687	0.4042677	-0.1117137	0.0499764	1771	-0.03 (-0.11, 0.05)
PUFA 20:5	Hannum	-0.1048317	0.0575975	-0.2139527	0.0042893	1771	-0.1 (-0.21, 0)
PUFA 20:5	Lin	-0.0247640	0.6834920	-0.1597916	0.1102635	1771	-0.02 (-0.16, 0.11)
PUFA 20:5	Zhang	-0.0240997	0.1077302	-0.0547871	0.0065877	1771	-0.02 (-0.05, 0.01)
PUFA 20:5	VidalBralo	-0.0806189	0.1066002	-0.1828860	0.0216482	1771	-0.08 (-0.18, 0.02)
PUFA 20:5	PhenoAge	-0.1322891	0.0503921	-0.2648660	0.0002877	1771	-0.13 (-0.26, 0)
PUFA 20:5	GrimAge2	-0.0035396	0.8861659	-0.0587698	0.0516905	1771	0 (-0.06, 0.05)
PUFA 20:5	DNAmTL	0.0047717	0.0071457	0.0017016	0.0078418	1771	0 (0, 0.01)
PUFA 20:5	YangCell	-0.0126895	0.0717782	-0.0267983	0.0014193	1771	-0.01 (-0.03, 0)
PUFA 20:5	DunedinPoAm	0.0053933	0.4840774	-0.0115579	0.0223446	1771	0.01 (-0.01, 0.02)
PUFA 22:5	Horvath1	-0.1101862	0.0573256	-0.2247239	0.0043516	1771	-0.11 (-0.22, 0)
PUFA 22:5	Horvath2	-0.0540915	0.0984214	-0.1207981	0.0126152	1771	-0.05 (-0.12, 0.01)
PUFA 22:5	Hannum	-0.1154233	0.0333925	-0.2191969	-0.0116497	1771	-0.12 (-0.22, -0.01)
PUFA 22:5	Lin	-0.0607910	0.3341465	-0.1971933	0.0756113	1771	-0.06 (-0.2, 0.08)
PUFA 22:5	Zhang	-0.0289876	0.0362240	-0.0555917	-0.0023835	1771	-0.03 (-0.06, 0)
PUFA 22:5	VidalBralo	-0.0723647	0.0941420	-0.1602483	0.0155189	1771	-0.07 (-0.16, 0.02)
PUFA 22:5	PhenoAge	-0.1296811	0.0488245	-0.2585108	-0.0008514	1771	-0.13 (-0.26, 0)
PUFA 22:5	GrimAge2	-0.0455059	0.1803985	-0.1170089	0.0259970	1771	-0.05 (-0.12, 0.03)
PUFA 22:5	DNAmTL	0.0039215	0.0263017	0.0005953	0.0072478	1771	0 (0, 0.01)
PUFA 22:5	YangCell	-0.0096009	0.2030655	-0.0255712	0.0063694	1771	-0.01 (-0.03, 0.01)
PUFA 22:5	DunedinPoAm	0.0034566	0.6838781	-0.0154155	0.0223287	1771	0 (-0.02, 0.02)
PUFA 22:6	Horvath1	-0.1529798	0.0176983	-0.2715043	-0.0344553	1771	-0.15 (-0.27, -0.03)
PUFA 22:6	Horvath2	-0.1047837	0.0179933	-0.1862635	-0.0233039	1771	-0.1 (-0.19, -0.02)
PUFA 22:6	Hannum	-0.1810825	0.0109711	-0.3078860	-0.0542791	1771	-0.18 (-0.31, -0.05)
PUFA 22:6	Lin	-0.1214425	0.1030791	-0.2736597	0.0307746	1771	-0.12 (-0.27, 0.03)
PUFA 22:6	Zhang	-0.0399050	0.0375497	-0.0768682	-0.0029418	1771	-0.04 (-0.08, 0)
PUFA 22:6	VidalBralo	-0.1000921	0.0526053	-0.2016169	0.0014327	1771	-0.1 (-0.2, 0)
PUFA 22:6	PhenoAge	-0.2053973	0.0082913	-0.3414121	-0.0693826	1771	-0.21 (-0.34, -0.07)
PUFA 22:6	GrimAge2	-0.0336983	0.1983232	-0.0891005	0.0217038	1771	-0.03 (-0.09, 0.02)
PUFA 22:6	DNAmTL	0.0037059	0.0315765	0.0004201	0.0069917	1771	0 (0, 0.01)
PUFA 22:6	YangCell	-0.0174201	0.0619188	-0.0359409	0.0011007	1771	-0.02 (-0.04, 0)
PUFA 22:6	DunedinPoAm	0.0047027	0.5900002	-0.0146194	0.0240248	1771	0 (-0.01, 0.02)

Interaction with sex

Primary analyses

# dataframe to store results
fat_res_alcSmkAct_sexInt = data.frame(nutrient = rep(c('Total fat', 'Saturated fat', 'Monounsaturated fat', 'Polyunsaturated fat', 'P:S ratio', 'Omega-3', 'Omega-6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 7),
  p = NA)

# NHANES variable names
vars_fat = c('DRXTTFAT_log2', 'DRXTSFAT_log2', 'DRXTMFAT_log2', 'DRXTPFAT_log2', 'psatfatRatio', 'omega3_log2', 'omega6_log2')
vars_ead = c('HorvathAge', 'SkinBloodAge', 'HannumAge', 'LinAge', 'ZhangAge', 'VidalBraloAge', 'PhenoAge', 'GrimAge2Mort', 'HorvathTelo', 'YangCell_scale', 'DunedinPoAm_scale')

# functions to fill in table
popTable1_sexInt = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", vars_fat[j], "*factor(RIAGENDR) +
        RIDAGEYR + I(RIDAGEYR^2) + factor(RIDRETH1) +
        BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
        factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
        design = nhanesDesign_use, 
        family = gaussian())
    tab[k,3] = summary(mod)$coef[23,4]
    return(tab)
}

popTable2_sexInt = function(tab, vars_fat, vars_ead){
    k = 1
    for (j in 1:length(vars_fat)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_sexInt(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

fat_res_alcSmkAct_sexInt = popTable2_sexInt(fat_res_alcSmkAct_sexInt, vars_fat, vars_ead)

write.csv(fat_res_alcSmkAct_sexInt, 'fat_res_alcSmkAct_sexInt.csv')

nutrient	clock	p
Total fat	Horvath1	0.6067637
Total fat	Horvath2	0.6490660
Total fat	Hannum	0.8886131
Total fat	Lin	0.7950334
Total fat	Zhang	0.3754884
Total fat	VidalBralo	0.1187233
Total fat	PhenoAge	0.3787310
Total fat	GrimAge2	0.2872691
Total fat	DNAmTL	0.8577423
Total fat	YangCell	0.3768410
Total fat	DunedinPoAm	0.4624077
Saturated fat	Horvath1	0.4172061
Saturated fat	Horvath2	0.2504841
Saturated fat	Hannum	0.7584096
Saturated fat	Lin	0.5220103
Saturated fat	Zhang	0.1796193
Saturated fat	VidalBralo	0.2074448
Saturated fat	PhenoAge	0.5653681
Saturated fat	GrimAge2	0.4771128
Saturated fat	DNAmTL	0.5790971
Saturated fat	YangCell	0.2371002
Saturated fat	DunedinPoAm	0.3896276
Monounsaturated fat	Horvath1	0.8686185
Monounsaturated fat	Horvath2	0.8451223
Monounsaturated fat	Hannum	0.7127280
Monounsaturated fat	Lin	0.9611956
Monounsaturated fat	Zhang	0.5619003
Monounsaturated fat	VidalBralo	0.1197849
Monounsaturated fat	PhenoAge	0.2582720
Monounsaturated fat	GrimAge2	0.2368331
Monounsaturated fat	DNAmTL	0.8767405
Monounsaturated fat	YangCell	0.2894760
Monounsaturated fat	DunedinPoAm	0.3075176
Polyunsaturated fat	Horvath1	0.9718485
Polyunsaturated fat	Horvath2	0.3760824
Polyunsaturated fat	Hannum	0.7428472
Polyunsaturated fat	Lin	0.7578479
Polyunsaturated fat	Zhang	0.8210564
Polyunsaturated fat	VidalBralo	0.1213662
Polyunsaturated fat	PhenoAge	0.4592610
Polyunsaturated fat	GrimAge2	0.1780528
Polyunsaturated fat	DNAmTL	0.7432926
Polyunsaturated fat	YangCell	0.3598273
Polyunsaturated fat	DunedinPoAm	0.2033248
P:S ratio	Horvath1	0.6378605
P:S ratio	Horvath2	0.1811604
P:S ratio	Hannum	0.6102786
P:S ratio	Lin	0.6089125
P:S ratio	Zhang	0.6123126
P:S ratio	VidalBralo	0.6499710
P:S ratio	PhenoAge	0.3940529
P:S ratio	GrimAge2	0.4859180
P:S ratio	DNAmTL	0.4003933
P:S ratio	YangCell	0.9227175
P:S ratio	DunedinPoAm	0.8620777
Omega-3	Horvath1	0.9313769
Omega-3	Horvath2	0.7394551
Omega-3	Hannum	0.7367328
Omega-3	Lin	0.9404485
Omega-3	Zhang	0.3356990
Omega-3	VidalBralo	0.0584009
Omega-3	PhenoAge	0.5814665
Omega-3	GrimAge2	0.2852819
Omega-3	DNAmTL	0.6045505
Omega-3	YangCell	0.4853449
Omega-3	DunedinPoAm	0.6359630
Omega-6	Horvath1	0.9907327
Omega-6	Horvath2	0.3358864
Omega-6	Hannum	0.6617755
Omega-6	Lin	0.6752303
Omega-6	Zhang	0.6957869
Omega-6	VidalBralo	0.1340185
Omega-6	PhenoAge	0.4275045
Omega-6	GrimAge2	0.1881826
Omega-6	DNAmTL	0.6599479
Omega-6	YangCell	0.3358852
Omega-6	DunedinPoAm	0.1795745

Stratified analyses - male

# dataframe to store results
fat_res_alcSmkAct_male = data.frame(nutrient = rep(c('Total fat', 'Saturated fat', 'Monounsaturated fat', 'Polyunsaturated fat', 'P:S ratio', 'Omega-3', 'Omega-6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 7),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA)

# NHANES variable names
vars_fat = c('DRXTTFAT_log2', 'DRXTSFAT_log2', 'DRXTMFAT_log2', 'DRXTPFAT_log2', 'psatfatRatio', 'omega3_log2', 'omega6_log2')
vars_ead = c('HorvathAge', 'SkinBloodAge', 'HannumAge', 'LinAge', 'ZhangAge', 'VidalBraloAge', 'PhenoAge', 'GrimAge2Mort', 'HorvathTelo', 'YangCell_scale', 'DunedinPoAm_scale')

# functions to fill in table
popTable1_male = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", vars_fat[j], " +
        RIDAGEYR + I(RIDAGEYR^2) + factor(RIDRETH1) +
        BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
        factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
        design = nhanesDesign_male, 
        family = gaussian())
    tab[k,3] = summary(mod)$coef[2,1]
    tab[k,4] = summary(mod)$coef[2,4]
    tab[k,5] = confint(mod)[2,1]
    tab[k,6] = confint(mod)[2,2]
    tab[k,7] = nrow(mod$model)
    return(tab)
}

popTable2_male = function(tab, vars_fat, vars_ead){
    k = 1
    for (j in 1:length(vars_fat)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_male(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

fat_res_alcSmkAct_male = popTable2_male(fat_res_alcSmkAct_male, vars_fat, vars_ead)

fat_res_alcSmkAct_male$B_CI = paste0(round(fat_res_alcSmkAct_male$coef, 2), ' (', round(fat_res_alcSmkAct_male$CI_L, 2), ', ', round(fat_res_alcSmkAct_male$CI_H, 2), ')')

write.csv(fat_res_alcSmkAct_male, 'fat_res_alcSmkAct_male.csv')

nutrient	clock	coef	p	CI_L	CI_H	n	B_CI
Total fat	Horvath1	-0.3404000	0.4197220	-1.2511095	0.5703095	952	-0.34 (-1.25, 0.57)
Total fat	Horvath2	0.2715930	0.5283711	-0.6652837	1.2084697	952	0.27 (-0.67, 1.21)
Total fat	Hannum	0.3558068	0.4299589	-0.6182065	1.3298201	952	0.36 (-0.62, 1.33)
Total fat	Lin	-0.6590626	0.3090716	-2.0420721	0.7239468	952	-0.66 (-2.04, 0.72)
Total fat	Zhang	0.2330226	0.1111501	-0.0654140	0.5314592	952	0.23 (-0.07, 0.53)
Total fat	VidalBralo	0.1233472	0.7823332	-0.8568182	1.1035127	952	0.12 (-0.86, 1.1)
Total fat	PhenoAge	-0.1958391	0.7055528	-1.3315691	0.9398908	952	-0.2 (-1.33, 0.94)
Total fat	GrimAge2	0.5453936	0.2210539	-0.3928908	1.4836781	952	0.55 (-0.39, 1.48)
Total fat	DNAmTL	-0.0139106	0.4600383	-0.0546846	0.0268635	952	-0.01 (-0.05, 0.03)
Total fat	YangCell	-0.0687745	0.3252387	-0.2182934	0.0807444	952	-0.07 (-0.22, 0.08)
Total fat	DunedinPoAm	0.1297063	0.1652921	-0.0645923	0.3240049	952	0.13 (-0.06, 0.32)
Saturated fat	Horvath1	0.2024294	0.5649309	-0.5640338	0.9688925	952	0.2 (-0.56, 0.97)
Saturated fat	Horvath2	0.6956918	0.0310785	0.0790796	1.3123040	952	0.7 (0.08, 1.31)
Saturated fat	Hannum	0.6300263	0.0582227	-0.0271057	1.2871582	952	0.63 (-0.03, 1.29)
Saturated fat	Lin	-0.1110294	0.8388317	-1.3107314	1.0886727	952	-0.11 (-1.31, 1.09)
Saturated fat	Zhang	0.2735206	0.0237003	0.0458404	0.5012007	952	0.27 (0.05, 0.5)
Saturated fat	VidalBralo	0.3979958	0.3100190	-0.4389155	1.2349070	952	0.4 (-0.44, 1.23)
Saturated fat	PhenoAge	0.3731882	0.4211694	-0.6284774	1.3748539	952	0.37 (-0.63, 1.37)
Saturated fat	GrimAge2	0.5381766	0.0924497	-0.1085682	1.1849213	952	0.54 (-0.11, 1.18)
Saturated fat	DNAmTL	-0.0166545	0.1753029	-0.0422621	0.0089530	952	-0.02 (-0.04, 0.01)
Saturated fat	YangCell	-0.0456753	0.4118903	-0.1657612	0.0744107	952	-0.05 (-0.17, 0.07)
Saturated fat	DunedinPoAm	0.0772615	0.2322265	-0.0591803	0.2137032	952	0.08 (-0.06, 0.21)
Monounsaturated fat	Horvath1	-0.5235858	0.2069335	-1.3945233	0.3473517	952	-0.52 (-1.39, 0.35)
Monounsaturated fat	Horvath2	0.0922864	0.7944285	-0.6854783	0.8700510	952	0.09 (-0.69, 0.87)
Monounsaturated fat	Hannum	0.3827337	0.3387291	-0.4742217	1.2396890	952	0.38 (-0.47, 1.24)
Monounsaturated fat	Lin	-0.6880315	0.2634630	-1.9929443	0.6168813	952	-0.69 (-1.99, 0.62)
Monounsaturated fat	Zhang	0.1298310	0.3290129	-0.1547644	0.4144264	952	0.13 (-0.15, 0.41)
Monounsaturated fat	VidalBralo	0.2377659	0.5626864	-0.6572243	1.1327561	952	0.24 (-0.66, 1.13)
Monounsaturated fat	PhenoAge	-0.0841003	0.8627177	-1.1533253	0.9851246	952	-0.08 (-1.15, 0.99)
Monounsaturated fat	GrimAge2	0.4697609	0.1946108	-0.2885095	1.2280314	952	0.47 (-0.29, 1.23)
Monounsaturated fat	DNAmTL	-0.0181438	0.2906811	-0.0547070	0.0184194	952	-0.02 (-0.05, 0.02)
Monounsaturated fat	YangCell	-0.0349015	0.5378071	-0.1581675	0.0883644	952	-0.03 (-0.16, 0.09)
Monounsaturated fat	DunedinPoAm	0.0998822	0.1813515	-0.0560854	0.2558498	952	0.1 (-0.06, 0.26)
Polyunsaturated fat	Horvath1	-0.6345703	0.0523534	-1.2771374	0.0079968	952	-0.63 (-1.28, 0.01)
Polyunsaturated fat	Horvath2	-0.4730048	0.1483504	-1.1498252	0.2038155	952	-0.47 (-1.15, 0.2)
Polyunsaturated fat	Hannum	-0.3253178	0.3118978	-1.0122244	0.3615887	952	-0.33 (-1.01, 0.36)
Polyunsaturated fat	Lin	-0.7903121	0.0657036	-1.6438920	0.0632678	952	-0.79 (-1.64, 0.06)
Polyunsaturated fat	Zhang	-0.0517761	0.5867647	-0.2595775	0.1560253	952	-0.05 (-0.26, 0.16)
Polyunsaturated fat	VidalBralo	-0.3201325	0.2596101	-0.9220424	0.2817774	952	-0.32 (-0.92, 0.28)
Polyunsaturated fat	PhenoAge	-0.5135960	0.1954908	-1.3444237	0.3172316	952	-0.51 (-1.34, 0.32)
Polyunsaturated fat	GrimAge2	-0.0745450	0.7713720	-0.6376224	0.4885324	952	-0.07 (-0.64, 0.49)
Polyunsaturated fat	DNAmTL	0.0027592	0.8259244	-0.0248067	0.0303252	952	0 (-0.02, 0.03)
Polyunsaturated fat	YangCell	-0.0093974	0.8448510	-0.1149418	0.0961469	952	-0.01 (-0.11, 0.1)
Polyunsaturated fat	DunedinPoAm	0.0072859	0.8799019	-0.0987446	0.1133164	952	0.01 (-0.1, 0.11)
P:S ratio	Horvath1	-0.5813568	0.2800221	-1.7255465	0.5628328	952	-0.58 (-1.73, 0.56)
P:S ratio	Horvath2	-0.7668067	0.0828552	-1.6559826	0.1223691	952	-0.77 (-1.66, 0.12)
P:S ratio	Hannum	-0.5878771	0.1683237	-1.4755568	0.2998026	952	-0.59 (-1.48, 0.3)
P:S ratio	Lin	-0.4038916	0.5191029	-1.7656557	0.9578726	952	-0.4 (-1.77, 0.96)
P:S ratio	Zhang	-0.1465116	0.3213131	-0.4623127	0.1692895	952	-0.15 (-0.46, 0.17)
P:S ratio	VidalBralo	-0.9072750	0.0697245	-1.9046350	0.0900850	952	-0.91 (-1.9, 0.09)
P:S ratio	PhenoAge	-0.5244095	0.4136860	-1.9086661	0.8598470	952	-0.52 (-1.91, 0.86)
P:S ratio	GrimAge2	-0.5582078	0.1177080	-1.2881756	0.1717601	952	-0.56 (-1.29, 0.17)
P:S ratio	DNAmTL	0.0090014	0.5800580	-0.0264764	0.0444793	952	0.01 (-0.03, 0.04)
P:S ratio	YangCell	0.0577751	0.4358225	-0.1024854	0.2180355	952	0.06 (-0.1, 0.22)
P:S ratio	DunedinPoAm	-0.0509880	0.4382955	-0.1932140	0.0912381	952	-0.05 (-0.19, 0.09)
Omega-3	Horvath1	-0.6244621	0.0399624	-1.2133005	-0.0356238	952	-0.62 (-1.21, -0.04)
Omega-3	Horvath2	-0.2587715	0.2687451	-0.7554103	0.2378673	952	-0.26 (-0.76, 0.24)
Omega-3	Hannum	-0.3014968	0.2869400	-0.9040628	0.3010693	952	-0.3 (-0.9, 0.3)
Omega-3	Lin	-0.6122692	0.1884455	-1.5855619	0.3610234	952	-0.61 (-1.59, 0.36)
Omega-3	Zhang	0.0411247	0.5786164	-0.1203365	0.2025858	952	0.04 (-0.12, 0.2)
Omega-3	VidalBralo	-0.6352447	0.0382261	-1.2275622	-0.0429271	952	-0.64 (-1.23, -0.04)
Omega-3	PhenoAge	-0.6302599	0.1145212	-1.4462107	0.1856909	952	-0.63 (-1.45, 0.19)
Omega-3	GrimAge2	-0.0977532	0.6858089	-0.6268966	0.4313902	952	-0.1 (-0.63, 0.43)
Omega-3	DNAmTL	-0.0002512	0.9827113	-0.0257611	0.0252586	952	0 (-0.03, 0.03)
Omega-3	YangCell	-0.0272018	0.4878155	-0.1122666	0.0578630	952	-0.03 (-0.11, 0.06)
Omega-3	DunedinPoAm	0.0471867	0.2798627	-0.0456502	0.1400236	952	0.05 (-0.05, 0.14)
Omega-6	Horvath1	-0.6196996	0.0511155	-1.2431204	0.0037212	952	-0.62 (-1.24, 0)
Omega-6	Horvath2	-0.4782390	0.1320372	-1.1314026	0.1749247	952	-0.48 (-1.13, 0.17)
Omega-6	Hannum	-0.3339845	0.2979825	-1.0179579	0.3499889	952	-0.33 (-1.02, 0.35)
Omega-6	Lin	-0.8301069	0.0521373	-1.6697229	0.0095092	952	-0.83 (-1.67, 0.01)
Omega-6	Zhang	-0.0596952	0.5165534	-0.2597121	0.1403217	952	-0.06 (-0.26, 0.14)
Omega-6	VidalBralo	-0.2936633	0.3016233	-0.8999009	0.3125743	952	-0.29 (-0.9, 0.31)
Omega-6	PhenoAge	-0.4863030	0.2014936	-1.2846292	0.3120231	952	-0.49 (-1.28, 0.31)
Omega-6	GrimAge2	-0.0725807	0.7817327	-0.6477004	0.5025389	952	-0.07 (-0.65, 0.5)
Omega-6	DNAmTL	0.0038471	0.7578240	-0.0235301	0.0312244	952	0 (-0.02, 0.03)
Omega-6	YangCell	-0.0041076	0.9291298	-0.1057040	0.0974887	952	0 (-0.11, 0.1)
Omega-6	DunedinPoAm	0.0006856	0.9888204	-0.1069673	0.1083384	952	0 (-0.11, 0.11)

Stratified analyses - female

# dataframe to store results
fat_res_alcSmkAct_female = data.frame(nutrient = rep(c('Total fat', 'Saturated fat', 'Monounsaturated fat', 'Polyunsaturated fat', 'P:S ratio', 'Omega-3', 'Omega-6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 7),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA)

# NHANES variable names
vars_fat = c('DRXTTFAT_log2', 'DRXTSFAT_log2', 'DRXTMFAT_log2', 'DRXTPFAT_log2', 'psatfatRatio', 'omega3_log2', 'omega6_log2')
vars_ead = c('HorvathAge', 'SkinBloodAge', 'HannumAge', 'LinAge', 'ZhangAge', 'VidalBraloAge', 'PhenoAge', 'GrimAge2Mort', 'HorvathTelo', 'YangCell_scale', 'DunedinPoAm_scale')

# functions to fill in table
popTable1_female = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", vars_fat[j], " +
        RIDAGEYR + I(RIDAGEYR^2) + factor(RIDRETH1) +
        BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
        factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
        design = nhanesDesign_female, 
        family = gaussian())
    tab[k,3] = summary(mod)$coef[2,1]
    tab[k,4] = summary(mod)$coef[2,4]
    tab[k,5] = confint(mod)[2,1]
    tab[k,6] = confint(mod)[2,2]
    tab[k,7] = nrow(mod$model)
    return(tab)
}

popTable2_female = function(tab, vars_fat, vars_ead){
    k = 1
    for (j in 1:length(vars_fat)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_female(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

fat_res_alcSmkAct_female = popTable2_female(fat_res_alcSmkAct_female, vars_fat, vars_ead)

fat_res_alcSmkAct_female$B_CI = paste0(round(fat_res_alcSmkAct_female$coef, 2), ' (', round(fat_res_alcSmkAct_female$CI_L, 2), ', ', round(fat_res_alcSmkAct_female$CI_H, 2), ')')

write.csv(fat_res_alcSmkAct_female, 'fat_res_alcSmkAct_female.csv')

nutrient	clock	coef	p	CI_L	CI_H	n	B_CI
Total fat	Horvath1	-0.8101238	0.1757952	-2.0573267	0.4370790	819	-0.81 (-2.06, 0.44)
Total fat	Horvath2	-0.3858231	0.3573863	-1.2856000	0.5139539	819	-0.39 (-1.29, 0.51)
Total fat	Hannum	-0.8703248	0.1436970	-2.0995644	0.3589149	819	-0.87 (-2.1, 0.36)
Total fat	Lin	-0.5151432	0.5127280	-2.2251816	1.1948952	819	-0.52 (-2.23, 1.19)
Total fat	Zhang	-0.2204645	0.1845989	-0.5675605	0.1266316	819	-0.22 (-0.57, 0.13)
Total fat	VidalBralo	-0.0402844	0.9520281	-1.5135005	1.4329316	819	-0.04 (-1.51, 1.43)
Total fat	PhenoAge	-0.3242905	0.6434950	-1.8564274	1.2078464	819	-0.32 (-1.86, 1.21)
Total fat	GrimAge2	0.1008623	0.8037636	-0.7906805	0.9924052	819	0.1 (-0.79, 0.99)
Total fat	DNAmTL	0.0116521	0.5600993	-0.0319137	0.0552178	819	0.01 (-0.03, 0.06)
Total fat	YangCell	-0.0380943	0.6290026	-0.2104101	0.1342214	819	-0.04 (-0.21, 0.13)
Total fat	DunedinPoAm	-0.0237408	0.7327218	-0.1761825	0.1287010	819	-0.02 (-0.18, 0.13)
Saturated fat	Horvath1	-0.1691938	0.6033927	-0.8801491	0.5417615	819	-0.17 (-0.88, 0.54)
Saturated fat	Horvath2	-0.2672648	0.3236664	-0.8463174	0.3117877	819	-0.27 (-0.85, 0.31)
Saturated fat	Hannum	-0.2535320	0.5195196	-1.1092163	0.6021524	819	-0.25 (-1.11, 0.6)
Saturated fat	Lin	-0.3249816	0.5134934	-1.4057815	0.7558183	819	-0.32 (-1.41, 0.76)
Saturated fat	Zhang	-0.1234302	0.2559940	-0.3536127	0.1067522	819	-0.12 (-0.35, 0.11)
Saturated fat	VidalBralo	0.2253878	0.5848901	-0.6746056	1.1253812	819	0.23 (-0.67, 1.13)
Saturated fat	PhenoAge	0.1530517	0.7931807	-1.1288336	1.4349369	819	0.15 (-1.13, 1.43)
Saturated fat	GrimAge2	0.0994519	0.7283915	-0.5284763	0.7273801	819	0.1 (-0.53, 0.73)
Saturated fat	DNAmTL	0.0168993	0.1988345	-0.0106635	0.0444621	819	0.02 (-0.01, 0.04)
Saturated fat	YangCell	-0.0753305	0.2340625	-0.2089304	0.0582694	819	-0.08 (-0.21, 0.06)
Saturated fat	DunedinPoAm	0.0244171	0.6984617	-0.1136602	0.1624943	819	0.02 (-0.11, 0.16)
Monounsaturated fat	Horvath1	-0.5121956	0.3083122	-1.5852228	0.5608316	819	-0.51 (-1.59, 0.56)
Monounsaturated fat	Horvath2	-0.2004656	0.4967291	-0.8407651	0.4398339	819	-0.2 (-0.84, 0.44)
Monounsaturated fat	Hannum	-0.3795011	0.3929789	-1.3363559	0.5773537	819	-0.38 (-1.34, 0.58)
Monounsaturated fat	Lin	-0.2568472	0.6723269	-1.5860758	1.0723814	819	-0.26 (-1.59, 1.07)
Monounsaturated fat	Zhang	-0.1563287	0.2418432	-0.4386042	0.1259467	819	-0.16 (-0.44, 0.13)
Monounsaturated fat	VidalBralo	0.2844612	0.6229878	-0.9797453	1.5486678	819	0.28 (-0.98, 1.55)
Monounsaturated fat	PhenoAge	0.3499990	0.5273504	-0.8542923	1.5542904	819	0.35 (-0.85, 1.55)
Monounsaturated fat	GrimAge2	0.2582221	0.4248974	-0.4406719	0.9571160	819	0.26 (-0.44, 0.96)
Monounsaturated fat	DNAmTL	-0.0015852	0.9225072	-0.0374312	0.0342608	819	0 (-0.04, 0.03)
Monounsaturated fat	YangCell	-0.0308138	0.6420156	-0.1757417	0.1141140	819	-0.03 (-0.18, 0.11)
Monounsaturated fat	DunedinPoAm	0.0490010	0.4897858	-0.1049493	0.2029513	819	0.05 (-0.1, 0.2)
Polyunsaturated fat	Horvath1	-0.6667833	0.1030390	-1.4981854	0.1646187	819	-0.67 (-1.5, 0.16)
Polyunsaturated fat	Horvath2	-0.1669943	0.5352088	-0.7529556	0.4189669	819	-0.17 (-0.75, 0.42)
Polyunsaturated fat	Hannum	-0.8887346	0.0362111	-1.7062877	-0.0711816	819	-0.89 (-1.71, -0.07)
Polyunsaturated fat	Lin	-0.4002243	0.4025355	-1.4308535	0.6304050	819	-0.4 (-1.43, 0.63)
Polyunsaturated fat	Zhang	-0.0986587	0.3237381	-0.3124447	0.1151274	819	-0.1 (-0.31, 0.12)
Polyunsaturated fat	VidalBralo	-0.3124127	0.4313745	-1.1703624	0.5455370	819	-0.31 (-1.17, 0.55)
Polyunsaturated fat	PhenoAge	-0.6933506	0.1118876	-1.5834526	0.1967515	819	-0.69 (-1.58, 0.2)
Polyunsaturated fat	GrimAge2	-0.2021536	0.4446588	-0.7742317	0.3699245	819	-0.2 (-0.77, 0.37)
Polyunsaturated fat	DNAmTL	-0.0018226	0.8969672	-0.0327760	0.0291309	819	0 (-0.03, 0.03)
Polyunsaturated fat	YangCell	-0.0340726	0.5410187	-0.1553887	0.0872435	819	-0.03 (-0.16, 0.09)
Polyunsaturated fat	DunedinPoAm	-0.0170582	0.7837249	-0.1535082	0.1193918	819	-0.02 (-0.15, 0.12)
P:S ratio	Horvath1	-1.1130652	0.0145010	-1.9470715	-0.2790588	819	-1.11 (-1.95, -0.28)
P:S ratio	Horvath2	-0.2503538	0.4323715	-0.9394213	0.4387137	819	-0.25 (-0.94, 0.44)
P:S ratio	Hannum	-1.1077804	0.0322549	-2.0984285	-0.1171323	819	-1.11 (-2.1, -0.12)
P:S ratio	Lin	-0.3308374	0.5316333	-1.4813778	0.8197030	819	-0.33 (-1.48, 0.82)
P:S ratio	Zhang	-0.1336654	0.2066318	-0.3558439	0.0885131	819	-0.13 (-0.36, 0.09)
P:S ratio	VidalBralo	-0.6800984	0.1588013	-1.6814184	0.3212216	819	-0.68 (-1.68, 0.32)
P:S ratio	PhenoAge	-1.5037690	0.0313112	-2.8389821	-0.1685560	819	-1.5 (-2.84, -0.17)
P:S ratio	GrimAge2	-0.3981412	0.2437522	-1.1202028	0.3239205	819	-0.4 (-1.12, 0.32)
P:S ratio	DNAmTL	-0.0049574	0.7846323	-0.0447841	0.0348693	819	0 (-0.04, 0.03)
P:S ratio	YangCell	0.0302778	0.6972625	-0.1402221	0.2007778	819	0.03 (-0.14, 0.2)
P:S ratio	DunedinPoAm	-0.0583256	0.4620430	-0.2300778	0.1134267	819	-0.06 (-0.23, 0.11)
Omega-3	Horvath1	-0.5867292	0.0377513	-1.1320965	-0.0413619	819	-0.59 (-1.13, -0.04)
Omega-3	Horvath2	-0.2250786	0.3022237	-0.6903442	0.2401870	819	-0.23 (-0.69, 0.24)
Omega-3	Hannum	-1.0363191	0.0077807	-1.7243122	-0.3483260	819	-1.04 (-1.72, -0.35)
Omega-3	Lin	-0.4493382	0.2359637	-1.2497638	0.3510875	819	-0.45 (-1.25, 0.35)
Omega-3	Zhang	-0.1619766	0.0659027	-0.3370759	0.0131228	819	-0.16 (-0.34, 0.01)
Omega-3	VidalBralo	-0.3079473	0.3316526	-0.9868840	0.3709895	819	-0.31 (-0.99, 0.37)
Omega-3	PhenoAge	-0.9005597	0.0166831	-1.5952078	-0.2059117	819	-0.9 (-1.6, -0.21)
Omega-3	GrimAge2	-0.3193272	0.1649542	-0.7972492	0.1585948	819	-0.32 (-0.8, 0.16)
Omega-3	DNAmTL	0.0243001	0.0543708	-0.0005641	0.0491644	819	0.02 (0, 0.05)
Omega-3	YangCell	-0.0231610	0.5951026	-0.1182648	0.0719428	819	-0.02 (-0.12, 0.07)
Omega-3	DunedinPoAm	-0.0390772	0.4457142	-0.1499279	0.0717736	819	-0.04 (-0.15, 0.07)
Omega-6	Horvath1	-0.6195298	0.1283914	-1.4565697	0.2175102	819	-0.62 (-1.46, 0.22)
Omega-6	Horvath2	-0.1321335	0.6273136	-0.7268555	0.4625884	819	-0.13 (-0.73, 0.46)
Omega-6	Hannum	-0.7974253	0.0496533	-1.5933528	-0.0014978	819	-0.8 (-1.59, 0)
Omega-6	Lin	-0.3438145	0.4715473	-1.3787660	0.6911371	819	-0.34 (-1.38, 0.69)
Omega-6	Zhang	-0.0713282	0.4676365	-0.2841011	0.1414447	819	-0.07 (-0.28, 0.14)
Omega-6	VidalBralo	-0.2930781	0.4529311	-1.1382480	0.5520918	819	-0.29 (-1.14, 0.55)
Omega-6	PhenoAge	-0.6094791	0.1429093	-1.4683903	0.2494322	819	-0.61 (-1.47, 0.25)
Omega-6	GrimAge2	-0.1856138	0.4752395	-0.7491736	0.3779461	819	-0.19 (-0.75, 0.38)
Omega-6	DNAmTL	-0.0056750	0.6834587	-0.0361506	0.0248007	819	-0.01 (-0.04, 0.02)
Omega-6	YangCell	-0.0333838	0.5453005	-0.1535440	0.0867764	819	-0.03 (-0.15, 0.09)
Omega-6	DunedinPoAm	-0.0131491	0.8335729	-0.1506646	0.1243664	819	-0.01 (-0.15, 0.12)

Associations with individual fatty acids

# dataframe to store results
subtypes_res_sexInt = data.frame(nutrient = rep(c('SFA 4:0', 'SFA 6:0', 'SFA 8:0', 'SFA 10:0', 'SFA 12:0', 'SFA 14:0', 'SFA 16:0', 'SFA 18:0',
                            'MUFA 16:1', 'MUFA 18:1', 'MUFA 20:1', 'MUFA 22:1',
                            'PUFA 18:2', 'PUFA 20:4', 'PUFA 18:3', 'PUFA 20:5', 'PUFA 22:5', 'PUFA 22:6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 18),
  p = NA)

# NHANES variable names
vars_ead = c("HorvathAge", "SkinBloodAge", "HannumAge", "LinAge", "ZhangAge", "VidalBraloAge", "PhenoAge", "GrimAge2Mort", "HorvathTelo", "YangCell_scale", "DunedinPoAm_scale")
subtypes = c("DRXTS040_log2", "DRXTS060_log2", "DRXTS080_log2", "DRXTS100_log2", "DRXTS120_log2", "DRXTS140_log2", "DRXTS160_log2", "DRXTS180_log2", "DRXTM161_log2", 
        "DRXTM181_log2", "DRXTM201_log2", "DRXTM221_log2", "DRXTP182_log2", "DRXTP204_log2", "DRXTP183_log2", "DRXTP205_log2", "DRXTP225_log2", "DRXTP226_log2")

# functions to fill in table
popTable1_subtypes_sexInt = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", subtypes[j], "*factor(RIAGENDR) +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian())
    tab[k,3] = summary(mod)$coef[23,4]
    return(tab)
}

popTable2_subtypes_sexInt = function(tab, subtypes, vars_ead){
    k = 1
    for (j in 1:length(subtypes)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_subtypes_sexInt(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

subtypes_res_sexInt = popTable2_subtypes_sexInt(subtypes_res_sexInt, subtypes, vars_ead)

write.csv(subtypes_res_sexInt, 'subtypes_res_sexInt.csv')

nutrient	clock	p
SFA 4:0	Horvath1	0.3445673
SFA 4:0	Horvath2	0.1475117
SFA 4:0	Hannum	0.4330198
SFA 4:0	Lin	0.4780219
SFA 4:0	Zhang	0.1109668
SFA 4:0	VidalBralo	0.4654798
SFA 4:0	PhenoAge	0.9494580
SFA 4:0	GrimAge2	0.9332097
SFA 4:0	DNAmTL	0.1041489
SFA 4:0	YangCell	0.6950601
SFA 4:0	DunedinPoAm	0.6427629
SFA 6:0	Horvath1	0.4205515
SFA 6:0	Horvath2	0.1075808
SFA 6:0	Hannum	0.7047285
SFA 6:0	Lin	0.6920761
SFA 6:0	Zhang	0.0667321
SFA 6:0	VidalBralo	0.4102334
SFA 6:0	PhenoAge	0.5969024
SFA 6:0	GrimAge2	0.7218738
SFA 6:0	DNAmTL	0.4152631
SFA 6:0	YangCell	0.9822492
SFA 6:0	DunedinPoAm	0.5094147
SFA 8:0	Horvath1	0.2572810
SFA 8:0	Horvath2	0.0856458
SFA 8:0	Hannum	0.3006167
SFA 8:0	Lin	0.8282039
SFA 8:0	Zhang	0.1245569
SFA 8:0	VidalBralo	0.3121120
SFA 8:0	PhenoAge	0.8017223
SFA 8:0	GrimAge2	0.6955434
SFA 8:0	DNAmTL	0.3057596
SFA 8:0	YangCell	0.1602095
SFA 8:0	DunedinPoAm	0.6530076
SFA 10:0	Horvath1	0.3294111
SFA 10:0	Horvath2	0.0913935
SFA 10:0	Hannum	0.5281895
SFA 10:0	Lin	0.3039457
SFA 10:0	Zhang	0.0574072
SFA 10:0	VidalBralo	0.6275704
SFA 10:0	PhenoAge	0.9618705
SFA 10:0	GrimAge2	0.8204105
SFA 10:0	DNAmTL	0.2710602
SFA 10:0	YangCell	0.4141477
SFA 10:0	DunedinPoAm	0.8195533
SFA 12:0	Horvath1	0.5684516
SFA 12:0	Horvath2	0.1221310
SFA 12:0	Hannum	0.4662645
SFA 12:0	Lin	0.9367957
SFA 12:0	Zhang	0.1321197
SFA 12:0	VidalBralo	0.2289585
SFA 12:0	PhenoAge	0.4703455
SFA 12:0	GrimAge2	0.3884460
SFA 12:0	DNAmTL	0.7177400
SFA 12:0	YangCell	0.0655538
SFA 12:0	DunedinPoAm	0.6412702
SFA 14:0	Horvath1	0.3676147
SFA 14:0	Horvath2	0.1338889
SFA 14:0	Hannum	0.5787703
SFA 14:0	Lin	0.2263256
SFA 14:0	Zhang	0.0995346
SFA 14:0	VidalBralo	0.3635039
SFA 14:0	PhenoAge	0.7248028
SFA 14:0	GrimAge2	0.7390649
SFA 14:0	DNAmTL	0.2587237
SFA 14:0	YangCell	0.2375419
SFA 14:0	DunedinPoAm	0.5170574
SFA 16:0	Horvath1	0.5782149
SFA 16:0	Horvath2	0.3911071
SFA 16:0	Hannum	0.9580656
SFA 16:0	Lin	0.6946366
SFA 16:0	Zhang	0.3134003
SFA 16:0	VidalBralo	0.1651676
SFA 16:0	PhenoAge	0.4853862
SFA 16:0	GrimAge2	0.5096121
SFA 16:0	DNAmTL	0.7658727
SFA 16:0	YangCell	0.2967775
SFA 16:0	DunedinPoAm	0.3918223
SFA 18:0	Horvath1	0.5504023
SFA 18:0	Horvath2	0.3183319
SFA 18:0	Hannum	0.8110608
SFA 18:0	Lin	0.5286643
SFA 18:0	Zhang	0.1917628
SFA 18:0	VidalBralo	0.2104773
SFA 18:0	PhenoAge	0.4995991
SFA 18:0	GrimAge2	0.4909264
SFA 18:0	DNAmTL	0.4896668
SFA 18:0	YangCell	0.3404071
SFA 18:0	DunedinPoAm	0.3932820
MUFA 16:1	Horvath1	0.9035067
MUFA 16:1	Horvath2	0.7252084
MUFA 16:1	Hannum	0.6487766
MUFA 16:1	Lin	0.9535059
MUFA 16:1	Zhang	0.9495323
MUFA 16:1	VidalBralo	0.3475859
MUFA 16:1	PhenoAge	0.2633346
MUFA 16:1	GrimAge2	0.4091584
MUFA 16:1	DNAmTL	0.8665061
MUFA 16:1	YangCell	0.5731857
MUFA 16:1	DunedinPoAm	0.3234921
MUFA 18:1	Horvath1	0.8810524
MUFA 18:1	Horvath2	0.9059817
MUFA 18:1	Hannum	0.6487693
MUFA 18:1	Lin	0.9477775
MUFA 18:1	Zhang	0.5782983
MUFA 18:1	VidalBralo	0.1176596
MUFA 18:1	PhenoAge	0.2419711
MUFA 18:1	GrimAge2	0.2460999
MUFA 18:1	DNAmTL	0.8896380
MUFA 18:1	YangCell	0.2806710
MUFA 18:1	DunedinPoAm	0.3140945
MUFA 20:1	Horvath1	0.6694374
MUFA 20:1	Horvath2	0.5133554
MUFA 20:1	Hannum	0.6246056
MUFA 20:1	Lin	0.8130976
MUFA 20:1	Zhang	0.9748328
MUFA 20:1	VidalBralo	0.9258879
MUFA 20:1	PhenoAge	0.4046842
MUFA 20:1	GrimAge2	0.4104836
MUFA 20:1	DNAmTL	0.7223080
MUFA 20:1	YangCell	0.4152660
MUFA 20:1	DunedinPoAm	0.2826837
MUFA 22:1	Horvath1	0.3676922
MUFA 22:1	Horvath2	0.9177691
MUFA 22:1	Hannum	0.6993166
MUFA 22:1	Lin	0.4392148
MUFA 22:1	Zhang	0.8591609
MUFA 22:1	VidalBralo	0.8254567
MUFA 22:1	PhenoAge	0.2271961
MUFA 22:1	GrimAge2	0.2995129
MUFA 22:1	DNAmTL	0.9000863
MUFA 22:1	YangCell	0.1632196
MUFA 22:1	DunedinPoAm	0.3435950
PUFA 18:2	Horvath1	0.9861546
PUFA 18:2	Horvath2	0.3327623
PUFA 18:2	Hannum	0.6623937
PUFA 18:2	Lin	0.6857845
PUFA 18:2	Zhang	0.6946732
PUFA 18:2	VidalBralo	0.1373752
PUFA 18:2	PhenoAge	0.4338301
PUFA 18:2	GrimAge2	0.1914092
PUFA 18:2	DNAmTL	0.6549157
PUFA 18:2	YangCell	0.3302279
PUFA 18:2	DunedinPoAm	0.1774442
PUFA 20:4	Horvath1	0.8844797
PUFA 20:4	Horvath2	0.7964122
PUFA 20:4	Hannum	0.4636642
PUFA 20:4	Lin	0.2323567
PUFA 20:4	Zhang	0.8229421
PUFA 20:4	VidalBralo	0.8611575
PUFA 20:4	PhenoAge	0.2202833
PUFA 20:4	GrimAge2	0.6338312
PUFA 20:4	DNAmTL	0.8923361
PUFA 20:4	YangCell	0.6415804
PUFA 20:4	DunedinPoAm	0.5112594
PUFA 18:3	Horvath1	0.9774029
PUFA 18:3	Horvath2	0.9649362
PUFA 18:3	Hannum	0.7730777
PUFA 18:3	Lin	0.9086781
PUFA 18:3	Zhang	0.3023284
PUFA 18:3	VidalBralo	0.0870364
PUFA 18:3	PhenoAge	0.4686465
PUFA 18:3	GrimAge2	0.3246897
PUFA 18:3	DNAmTL	0.7827405
PUFA 18:3	YangCell	0.4213858
PUFA 18:3	DunedinPoAm	0.4404757
PUFA 20:5	Horvath1	0.4438446
PUFA 20:5	Horvath2	0.2271237
PUFA 20:5	Hannum	0.5212573
PUFA 20:5	Lin	0.8484463
PUFA 20:5	Zhang	0.5256270
PUFA 20:5	VidalBralo	0.2295399
PUFA 20:5	PhenoAge	0.8867630
PUFA 20:5	GrimAge2	0.8379242
PUFA 20:5	DNAmTL	0.5254133
PUFA 20:5	YangCell	0.3101918
PUFA 20:5	DunedinPoAm	0.7412640
PUFA 22:5	Horvath1	0.6851782
PUFA 22:5	Horvath2	0.2793731
PUFA 22:5	Hannum	0.4665826
PUFA 22:5	Lin	0.8907928
PUFA 22:5	Zhang	0.7089443
PUFA 22:5	VidalBralo	0.6091776
PUFA 22:5	PhenoAge	0.7208012
PUFA 22:5	GrimAge2	0.9928404
PUFA 22:5	DNAmTL	0.4354099
PUFA 22:5	YangCell	0.1628701
PUFA 22:5	DunedinPoAm	0.5353781
PUFA 22:6	Horvath1	0.3637890
PUFA 22:6	Horvath2	0.2418147
PUFA 22:6	Hannum	0.2698123
PUFA 22:6	Lin	0.1332951
PUFA 22:6	Zhang	0.2822182
PUFA 22:6	VidalBralo	0.2175572
PUFA 22:6	PhenoAge	0.5507195
PUFA 22:6	GrimAge2	0.9640465
PUFA 22:6	DNAmTL	0.7397228
PUFA 22:6	YangCell	0.4914307
PUFA 22:6	DunedinPoAm	0.9380648

Stratified analyses - male

# dataframe to store results
subtypes_res_male = data.frame(nutrient = rep(c('SFA 4:0', 'SFA 6:0', 'SFA 8:0', 'SFA 10:0', 'SFA 12:0', 'SFA 14:0', 'SFA 16:0', 'SFA 18:0',
                            'MUFA 16:1', 'MUFA 18:1', 'MUFA 20:1', 'MUFA 22:1',
                            'PUFA 18:2', 'PUFA 20:4', 'PUFA 18:3', 'PUFA 20:5', 'PUFA 22:5', 'PUFA 22:6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 18),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA)

# NHANES variable names
vars_ead = c("HorvathAge", "SkinBloodAge", "HannumAge", "LinAge", "ZhangAge", "VidalBraloAge", "PhenoAge", "GrimAge2Mort", "HorvathTelo", "YangCell_scale", "DunedinPoAm_scale")
subtypes = c("DRXTS040_log2", "DRXTS060_log2", "DRXTS080_log2", "DRXTS100_log2", "DRXTS120_log2", "DRXTS140_log2", "DRXTS160_log2", "DRXTS180_log2", "DRXTM161_log2", 
        "DRXTM181_log2", "DRXTM201_log2", "DRXTM221_log2", "DRXTP182_log2", "DRXTP204_log2", "DRXTP183_log2", "DRXTP205_log2", "DRXTP225_log2", "DRXTP226_log2")

# functions to fill in table
popTable1_subtypes_male = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", subtypes[j], " +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_male, 
            family = gaussian())
    tab[k,3] = summary(mod)$coef[2,1]
    tab[k,4] = summary(mod)$coef[2,4]
    tab[k,5] = confint(mod)[2,1]
    tab[k,6] = confint(mod)[2,2]
    tab[k,7] = nrow(mod$model)
    return(tab)
}

popTable2_subtypes_male = function(tab, subtypes, vars_ead){
    k = 1
    for (j in 1:length(subtypes)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_subtypes_male(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

subtypes_res_male = popTable2_subtypes_male(subtypes_res_male, subtypes, vars_ead)

subtypes_res_male$B_CI = paste0(round(subtypes_res_male$coef, 2), ' (', round(subtypes_res_male$CI_L, 2), ', ', round(subtypes_res_male$CI_H, 2), ')')

write.csv(subtypes_res_male, 'subtypes_res_male.csv')

nutrient	clock	coef	p	CI_L	CI_H	n	B_CI
SFA 4:0	Horvath1	0.0710569	0.2539632	-0.0608470	0.2029609	952	0.07 (-0.06, 0.2)
SFA 4:0	Horvath2	0.0852555	0.0839326	-0.0140148	0.1845258	952	0.09 (-0.01, 0.18)
SFA 4:0	Hannum	0.0908631	0.1651606	-0.0452016	0.2269279	952	0.09 (-0.05, 0.23)
SFA 4:0	Lin	0.0084676	0.9457715	-0.2654135	0.2823486	952	0.01 (-0.27, 0.28)
SFA 4:0	Zhang	0.0309677	0.1221682	-0.0100920	0.0720274	952	0.03 (-0.01, 0.07)
SFA 4:0	VidalBralo	0.0633703	0.3578837	-0.0845761	0.2113167	952	0.06 (-0.08, 0.21)
SFA 4:0	PhenoAge	0.0704813	0.3271995	-0.0834061	0.2243687	952	0.07 (-0.08, 0.22)
SFA 4:0	GrimAge2	0.0525781	0.3771106	-0.0754459	0.1806022	952	0.05 (-0.08, 0.18)
SFA 4:0	DNAmTL	-0.0042985	0.0593390	-0.0088063	0.0002092	952	0 (-0.01, 0)
SFA 4:0	YangCell	-0.0076829	0.4783838	-0.0311818	0.0158160	952	-0.01 (-0.03, 0.02)
SFA 4:0	DunedinPoAm	0.0079150	0.3372311	-0.0097491	0.0255791	952	0.01 (-0.01, 0.03)
SFA 6:0	Horvath1	0.0554592	0.4204554	-0.0931601	0.2040784	952	0.06 (-0.09, 0.2)
SFA 6:0	Horvath2	0.0920214	0.1018372	-0.0222527	0.2062955	952	0.09 (-0.02, 0.21)
SFA 6:0	Hannum	0.0741431	0.2749316	-0.0701356	0.2184218	952	0.07 (-0.07, 0.22)
SFA 6:0	Lin	-0.0231444	0.8620097	-0.3158673	0.2695785	952	-0.02 (-0.32, 0.27)
SFA 6:0	Zhang	0.0276069	0.2043666	-0.0180306	0.0732445	952	0.03 (-0.02, 0.07)
SFA 6:0	VidalBralo	0.0733512	0.3122471	-0.0816480	0.2283504	952	0.07 (-0.08, 0.23)
SFA 6:0	PhenoAge	0.0482649	0.5052609	-0.1090825	0.2056123	952	0.05 (-0.11, 0.21)
SFA 6:0	GrimAge2	0.0572132	0.3588238	-0.0766331	0.1910595	952	0.06 (-0.08, 0.19)
SFA 6:0	DNAmTL	-0.0024108	0.2773499	-0.0071273	0.0023058	952	0 (-0.01, 0)
SFA 6:0	YangCell	-0.0098859	0.4546739	-0.0385085	0.0187367	952	-0.01 (-0.04, 0.02)
SFA 6:0	DunedinPoAm	0.0055455	0.5698792	-0.0157267	0.0268178	952	0.01 (-0.02, 0.03)
SFA 8:0	Horvath1	0.0873106	0.2315805	-0.0666463	0.2412676	952	0.09 (-0.07, 0.24)
SFA 8:0	Horvath2	0.0943410	0.0677131	-0.0084644	0.1971464	952	0.09 (-0.01, 0.2)
SFA 8:0	Hannum	0.0587656	0.3841813	-0.0865621	0.2040933	952	0.06 (-0.09, 0.2)
SFA 8:0	Lin	-0.0377051	0.8218810	-0.4056792	0.3302689	952	-0.04 (-0.41, 0.33)
SFA 8:0	Zhang	0.0209049	0.3081421	-0.0228736	0.0646835	952	0.02 (-0.02, 0.06)
SFA 8:0	VidalBralo	0.0666349	0.3342223	-0.0811028	0.2143727	952	0.07 (-0.08, 0.21)
SFA 8:0	PhenoAge	-0.0196336	0.8261916	-0.2160893	0.1768221	952	-0.02 (-0.22, 0.18)
SFA 8:0	GrimAge2	0.0426299	0.5372004	-0.1077019	0.1929617	952	0.04 (-0.11, 0.19)
SFA 8:0	DNAmTL	-0.0013336	0.6091661	-0.0070292	0.0043621	952	0 (-0.01, 0)
SFA 8:0	YangCell	-0.0053830	0.6735350	-0.0333518	0.0225857	952	-0.01 (-0.03, 0.02)
SFA 8:0	DunedinPoAm	0.0073201	0.5163479	-0.0171945	0.0318346	952	0.01 (-0.02, 0.03)
SFA 10:0	Horvath1	0.0909298	0.3640883	-0.1242554	0.3061150	952	0.09 (-0.12, 0.31)
SFA 10:0	Horvath2	0.1333074	0.0902287	-0.0256075	0.2922223	952	0.13 (-0.03, 0.29)
SFA 10:0	Hannum	0.0577331	0.4696397	-0.1152871	0.2307533	952	0.06 (-0.12, 0.23)
SFA 10:0	Lin	-0.0298326	0.8742020	-0.4441302	0.3844651	952	-0.03 (-0.44, 0.38)
SFA 10:0	Zhang	0.0410767	0.1790330	-0.0226881	0.1048414	952	0.04 (-0.02, 0.1)
SFA 10:0	VidalBralo	0.0956345	0.3800020	-0.1387101	0.3299791	952	0.1 (-0.14, 0.33)
SFA 10:0	PhenoAge	0.0515856	0.6807617	-0.2229392	0.3261103	952	0.05 (-0.22, 0.33)
SFA 10:0	GrimAge2	0.1092423	0.2086477	-0.0732242	0.2917087	952	0.11 (-0.07, 0.29)
SFA 10:0	DNAmTL	-0.0011754	0.7167454	-0.0082767	0.0059258	952	0 (-0.01, 0.01)
SFA 10:0	YangCell	-0.0173516	0.2291664	-0.0477764	0.0130733	952	-0.02 (-0.05, 0.01)
SFA 10:0	DunedinPoAm	0.0264994	0.1001747	-0.0062219	0.0592206	952	0.03 (-0.01, 0.06)
SFA 12:0	Horvath1	0.0471653	0.6388276	-0.1725360	0.2668666	952	0.05 (-0.17, 0.27)
SFA 12:0	Horvath2	0.1500357	0.1091471	-0.0408721	0.3409436	952	0.15 (-0.04, 0.34)
SFA 12:0	Hannum	0.0446745	0.6399442	-0.1641281	0.2534772	952	0.04 (-0.16, 0.25)
SFA 12:0	Lin	-0.1762711	0.4863828	-0.7256395	0.3730973	952	-0.18 (-0.73, 0.37)
SFA 12:0	Zhang	0.0363000	0.2395870	-0.0289059	0.1015058	952	0.04 (-0.03, 0.1)
SFA 12:0	VidalBralo	0.0139983	0.9124392	-0.2660042	0.2940008	952	0.01 (-0.27, 0.29)
SFA 12:0	PhenoAge	-0.1289546	0.3816270	-0.4460762	0.1881671	952	-0.13 (-0.45, 0.19)
SFA 12:0	GrimAge2	0.0640888	0.5418772	-0.1645962	0.2927738	952	0.06 (-0.16, 0.29)
SFA 12:0	DNAmTL	0.0017164	0.6759242	-0.0072719	0.0107047	952	0 (-0.01, 0.01)
SFA 12:0	YangCell	-0.0102201	0.6355953	-0.0573659	0.0369256	952	-0.01 (-0.06, 0.04)
SFA 12:0	DunedinPoAm	0.0203004	0.2842759	-0.0200328	0.0606336	952	0.02 (-0.02, 0.06)
SFA 14:0	Horvath1	0.1720250	0.3386622	-0.2130894	0.5571394	952	0.17 (-0.21, 0.56)
SFA 14:0	Horvath2	0.3124745	0.0426427	0.0128796	0.6120693	952	0.31 (0.01, 0.61)
SFA 14:0	Hannum	0.2094242	0.1583989	-0.0985809	0.5174293	952	0.21 (-0.1, 0.52)
SFA 14:0	Lin	0.0254109	0.9311694	-0.6217741	0.6725959	952	0.03 (-0.62, 0.67)
SFA 14:0	Zhang	0.1007489	0.0699071	-0.0100906	0.2115883	952	0.1 (-0.01, 0.21)
SFA 14:0	VidalBralo	0.1549549	0.4664704	-0.3060316	0.6159414	952	0.15 (-0.31, 0.62)
SFA 14:0	PhenoAge	0.0945395	0.6751432	-0.3992681	0.5883471	952	0.09 (-0.4, 0.59)
SFA 14:0	GrimAge2	0.1935597	0.2228715	-0.1408658	0.5279852	952	0.19 (-0.14, 0.53)
SFA 14:0	DNAmTL	-0.0020457	0.7327002	-0.0151803	0.0110889	952	0 (-0.02, 0.01)
SFA 14:0	YangCell	-0.0340940	0.2703931	-0.0997698	0.0315818	952	-0.03 (-0.1, 0.03)
SFA 14:0	DunedinPoAm	0.0279313	0.3386207	-0.0345931	0.0904557	952	0.03 (-0.03, 0.09)
SFA 16:0	Horvath1	0.0696720	0.8653920	-0.8339190	0.9732629	952	0.07 (-0.83, 0.97)
SFA 16:0	Horvath2	0.6999673	0.0656927	-0.0559984	1.4559331	952	0.7 (-0.06, 1.46)
SFA 16:0	Hannum	0.7626052	0.0782989	-0.1060396	1.6312500	952	0.76 (-0.11, 1.63)
SFA 16:0	Lin	-0.1709355	0.7999428	-1.6522989	1.3104278	952	-0.17 (-1.65, 1.31)
SFA 16:0	Zhang	0.3013547	0.0365593	0.0234775	0.5792320	952	0.3 (0.02, 0.58)
SFA 16:0	VidalBralo	0.3855643	0.3537573	-0.5065125	1.2776411	952	0.39 (-0.51, 1.28)
SFA 16:0	PhenoAge	0.4983180	0.3456733	-0.6344716	1.6311076	952	0.5 (-0.63, 1.63)
SFA 16:0	GrimAge2	0.7107730	0.0753768	-0.0893269	1.5108728	952	0.71 (-0.09, 1.51)
SFA 16:0	DNAmTL	-0.0191708	0.2414575	-0.0537559	0.0154143	952	-0.02 (-0.05, 0.02)
SFA 16:0	YangCell	-0.0414280	0.5075417	-0.1772319	0.0943759	952	-0.04 (-0.18, 0.09)
SFA 16:0	DunedinPoAm	0.1077991	0.1726191	-0.0568057	0.2724038	952	0.11 (-0.06, 0.27)
SFA 18:0	Horvath1	0.0391235	0.9126011	-0.7449040	0.8231509	952	0.04 (-0.74, 0.82)
SFA 18:0	Horvath2	0.5827615	0.0652277	-0.0453089	1.2108319	952	0.58 (-0.05, 1.21)
SFA 18:0	Hannum	0.5796250	0.0635638	-0.0403636	1.1996136	952	0.58 (-0.04, 1.2)
SFA 18:0	Lin	-0.1180794	0.7853604	-1.0700168	0.8338581	952	-0.12 (-1.07, 0.83)
SFA 18:0	Zhang	0.2640068	0.0230143	0.0456874	0.4823261	952	0.26 (0.05, 0.48)
SFA 18:0	VidalBralo	0.4554651	0.2326873	-0.3497363	1.2606665	952	0.46 (-0.35, 1.26)
SFA 18:0	PhenoAge	0.3738335	0.4217239	-0.6308088	1.3784757	952	0.37 (-0.63, 1.38)
SFA 18:0	GrimAge2	0.5091010	0.0876225	-0.0919921	1.1101940	952	0.51 (-0.09, 1.11)
SFA 18:0	DNAmTL	-0.0224488	0.0511866	-0.0450409	0.0001433	952	-0.02 (-0.05, 0)
SFA 18:0	YangCell	-0.0490546	0.4069664	-0.1766227	0.0785135	952	-0.05 (-0.18, 0.08)
SFA 18:0	DunedinPoAm	0.0594369	0.3697485	-0.0829726	0.2018463	952	0.06 (-0.08, 0.2)
MUFA 16:1	Horvath1	-0.0549685	0.8374557	-0.6438077	0.5338706	952	-0.05 (-0.64, 0.53)
MUFA 16:1	Horvath2	0.3660508	0.1240055	-0.1220229	0.8541245	952	0.37 (-0.12, 0.85)
MUFA 16:1	Hannum	0.4401951	0.1320875	-0.1610984	1.0414885	952	0.44 (-0.16, 1.04)
MUFA 16:1	Lin	-0.1334734	0.7345344	-0.9966442	0.7296974	952	-0.13 (-1, 0.73)
MUFA 16:1	Zhang	0.0903726	0.2757405	-0.0858054	0.2665505	952	0.09 (-0.09, 0.27)
MUFA 16:1	VidalBralo	0.2750318	0.2912707	-0.2799329	0.8299965	952	0.28 (-0.28, 0.83)
MUFA 16:1	PhenoAge	0.1993212	0.5744428	-0.5745671	0.9732096	952	0.2 (-0.57, 0.97)
MUFA 16:1	GrimAge2	0.2792637	0.3454777	-0.3552950	0.9138225	952	0.28 (-0.36, 0.91)
MUFA 16:1	DNAmTL	-0.0023556	0.8253797	-0.0258146	0.0211034	952	0 (-0.03, 0.02)
MUFA 16:1	YangCell	-0.0412992	0.3753763	-0.1414781	0.0588798	952	-0.04 (-0.14, 0.06)
MUFA 16:1	DunedinPoAm	0.0623299	0.2198511	-0.0445971	0.1692569	952	0.06 (-0.04, 0.17)
MUFA 18:1	Horvath1	-0.5623068	0.1739226	-1.4238249	0.2992113	952	-0.56 (-1.42, 0.3)
MUFA 18:1	Horvath2	0.0252858	0.9443003	-0.7709347	0.8215064	952	0.03 (-0.77, 0.82)
MUFA 18:1	Hannum	0.2895748	0.4849875	-0.6099531	1.1891027	952	0.29 (-0.61, 1.19)
MUFA 18:1	Lin	-0.8102118	0.2088494	-2.1641585	0.5437349	952	-0.81 (-2.16, 0.54)
MUFA 18:1	Zhang	0.1204722	0.3680715	-0.1671194	0.4080639	952	0.12 (-0.17, 0.41)
MUFA 18:1	VidalBralo	0.1645530	0.6901493	-0.7394819	1.0685880	952	0.16 (-0.74, 1.07)
MUFA 18:1	PhenoAge	-0.1925175	0.6986079	-1.2817542	0.8967191	952	-0.19 (-1.28, 0.9)
MUFA 18:1	GrimAge2	0.4233167	0.2394077	-0.3367752	1.1834087	952	0.42 (-0.34, 1.18)
MUFA 18:1	DNAmTL	-0.0162534	0.3364246	-0.0524627	0.0199560	952	-0.02 (-0.05, 0.02)
MUFA 18:1	YangCell	-0.0326462	0.5678430	-0.1572041	0.0919117	952	-0.03 (-0.16, 0.09)
MUFA 18:1	DunedinPoAm	0.0968567	0.1932747	-0.0589706	0.2526840	952	0.1 (-0.06, 0.25)
MUFA 20:1	Horvath1	-0.1541145	0.3841859	-0.5352447	0.2270157	952	-0.15 (-0.54, 0.23)
MUFA 20:1	Horvath2	-0.1148107	0.4339801	-0.4319600	0.2023385	952	-0.11 (-0.43, 0.2)
MUFA 20:1	Hannum	-0.1084476	0.5314600	-0.4854287	0.2685335	952	-0.11 (-0.49, 0.27)
MUFA 20:1	Lin	-0.2902961	0.2379450	-0.8097860	0.2291938	952	-0.29 (-0.81, 0.23)
MUFA 20:1	Zhang	-0.0376579	0.4867299	-0.1551189	0.0798032	952	-0.04 (-0.16, 0.08)
MUFA 20:1	VidalBralo	0.1116869	0.4535705	-0.2108639	0.4342377	952	0.11 (-0.21, 0.43)
MUFA 20:1	PhenoAge	-0.1084486	0.5656451	-0.5198397	0.3029424	952	-0.11 (-0.52, 0.3)
MUFA 20:1	GrimAge2	0.1248312	0.3829827	-0.1830657	0.4327281	952	0.12 (-0.18, 0.43)
MUFA 20:1	DNAmTL	-0.0083457	0.2384852	-0.0232992	0.0066077	952	-0.01 (-0.02, 0.01)
MUFA 20:1	YangCell	-0.0009426	0.9660750	-0.0497034	0.0478182	952	0 (-0.05, 0.05)
MUFA 20:1	DunedinPoAm	0.0135638	0.6273274	-0.0474884	0.0746160	952	0.01 (-0.05, 0.07)
MUFA 22:1	Horvath1	-0.0340155	0.5725771	-0.1654317	0.0974006	952	-0.03 (-0.17, 0.1)
MUFA 22:1	Horvath2	0.0381044	0.5310560	-0.0942193	0.1704280	952	0.04 (-0.09, 0.17)
MUFA 22:1	Hannum	0.0342727	0.5895034	-0.1043102	0.1728556	952	0.03 (-0.1, 0.17)
MUFA 22:1	Lin	0.0289889	0.7305133	-0.1555542	0.2135319	952	0.03 (-0.16, 0.21)
MUFA 22:1	Zhang	0.0048714	0.8189360	-0.0418808	0.0516236	952	0 (-0.04, 0.05)
MUFA 22:1	VidalBralo	-0.0194604	0.7161302	-0.1367602	0.0978394	952	-0.02 (-0.14, 0.1)
MUFA 22:1	PhenoAge	0.0177521	0.8518719	-0.1912142	0.2267183	952	0.02 (-0.19, 0.23)
MUFA 22:1	GrimAge2	-0.0445571	0.4466651	-0.1712265	0.0821122	952	-0.04 (-0.17, 0.08)
MUFA 22:1	DNAmTL	-0.0003274	0.9063221	-0.0064466	0.0057918	952	0 (-0.01, 0.01)
MUFA 22:1	YangCell	0.0114746	0.3025420	-0.0122614	0.0352106	952	0.01 (-0.01, 0.04)
MUFA 22:1	DunedinPoAm	0.0037795	0.7102148	-0.0185117	0.0260707	952	0 (-0.02, 0.03)
PUFA 18:2	Horvath1	-0.6070840	0.0535821	-1.2257538	0.0115858	952	-0.61 (-1.23, 0.01)
PUFA 18:2	Horvath2	-0.4738536	0.1323061	-1.1215398	0.1738326	952	-0.47 (-1.12, 0.17)
PUFA 18:2	Hannum	-0.3321770	0.2971558	-1.0112110	0.3468570	952	-0.33 (-1.01, 0.35)
PUFA 18:2	Lin	-0.8131681	0.0549480	-1.6476552	0.0213191	952	-0.81 (-1.65, 0.02)
PUFA 18:2	Zhang	-0.0587042	0.5202868	-0.2572072	0.1397989	952	-0.06 (-0.26, 0.14)
PUFA 18:2	VidalBralo	-0.2906129	0.3035542	-0.8931059	0.3118801	952	-0.29 (-0.89, 0.31)
PUFA 18:2	PhenoAge	-0.4750068	0.2087198	-1.2685441	0.3185306	952	-0.48 (-1.27, 0.32)
PUFA 18:2	GrimAge2	-0.0736952	0.7768054	-0.6443558	0.4969653	952	-0.07 (-0.64, 0.5)
PUFA 18:2	DNAmTL	0.0038222	0.7570212	-0.0232841	0.0309284	952	0 (-0.02, 0.03)
PUFA 18:2	YangCell	-0.0025998	0.9545980	-0.1030635	0.0978639	952	0 (-0.1, 0.1)
PUFA 18:2	DunedinPoAm	-0.0013295	0.9780799	-0.1077940	0.1051350	952	0 (-0.11, 0.11)
PUFA 20:4	Horvath1	-0.0971210	0.2444029	-0.2735208	0.0792788	952	-0.1 (-0.27, 0.08)
PUFA 20:4	Horvath2	-0.0137108	0.8637044	-0.1893017	0.1618802	952	-0.01 (-0.19, 0.16)
PUFA 20:4	Hannum	-0.0641212	0.4951140	-0.2681412	0.1398988	952	-0.06 (-0.27, 0.14)
PUFA 20:4	Lin	-0.3470689	0.0124803	-0.5993652	-0.0947726	952	-0.35 (-0.6, -0.09)
PUFA 20:4	Zhang	-0.0232709	0.3661112	-0.0785857	0.0320438	952	-0.02 (-0.08, 0.03)
PUFA 20:4	VidalBralo	-0.0609871	0.4701934	-0.2439944	0.1220201	952	-0.06 (-0.24, 0.12)
PUFA 20:4	PhenoAge	-0.1760300	0.1265604	-0.4125605	0.0605006	952	-0.18 (-0.41, 0.06)
PUFA 20:4	GrimAge2	0.0280484	0.7450670	-0.1611732	0.2172700	952	0.03 (-0.16, 0.22)
PUFA 20:4	DNAmTL	-0.0010011	0.8240474	-0.0108942	0.0088920	952	0 (-0.01, 0.01)
PUFA 20:4	YangCell	-0.0090267	0.5479652	-0.0417381	0.0236848	952	-0.01 (-0.04, 0.02)
PUFA 20:4	DunedinPoAm	0.0196003	0.3200660	-0.0225324	0.0617330	952	0.02 (-0.02, 0.06)
PUFA 18:3	Horvath1	-0.5809260	0.0713670	-1.2240307	0.0621787	952	-0.58 (-1.22, 0.06)
PUFA 18:3	Horvath2	-0.1691337	0.5266016	-0.7500179	0.4117504	952	-0.17 (-0.75, 0.41)
PUFA 18:3	Hannum	-0.2268506	0.4279718	-0.8450834	0.3913822	952	-0.23 (-0.85, 0.39)
PUFA 18:3	Lin	-0.6447076	0.1974609	-1.6926931	0.4032779	952	-0.64 (-1.69, 0.4)
PUFA 18:3	Zhang	0.0864769	0.2896875	-0.0874083	0.2603622	952	0.09 (-0.09, 0.26)
PUFA 18:3	VidalBralo	-0.5040507	0.1200277	-1.1679733	0.1598720	952	-0.5 (-1.17, 0.16)
PUFA 18:3	PhenoAge	-0.6122204	0.1626189	-1.5228484	0.2984076	952	-0.61 (-1.52, 0.3)
PUFA 18:3	GrimAge2	-0.0793939	0.7512602	-0.6288986	0.4701108	952	-0.08 (-0.63, 0.47)
PUFA 18:3	DNAmTL	-0.0021787	0.8635855	-0.0300562	0.0256988	952	0 (-0.03, 0.03)
PUFA 18:3	YangCell	-0.0096099	0.8218540	-0.1033808	0.0841611	952	-0.01 (-0.1, 0.08)
PUFA 18:3	DunedinPoAm	0.0166897	0.7149998	-0.0834890	0.1168683	952	0.02 (-0.08, 0.12)
PUFA 20:5	Horvath1	-0.1059343	0.1736527	-0.2681248	0.0562561	952	-0.11 (-0.27, 0.06)
PUFA 20:5	Horvath2	-0.0672085	0.2469453	-0.1899908	0.0555738	952	-0.07 (-0.19, 0.06)
PUFA 20:5	Hannum	-0.1213201	0.1238523	-0.2830069	0.0403667	952	-0.12 (-0.28, 0.04)
PUFA 20:5	Lin	-0.0317283	0.7116997	-0.2198774	0.1564208	952	-0.03 (-0.22, 0.16)
PUFA 20:5	Zhang	-0.0277211	0.1838012	-0.0712771	0.0158349	952	-0.03 (-0.07, 0.02)
PUFA 20:5	VidalBralo	-0.1129307	0.0595938	-0.2315017	0.0056402	952	-0.11 (-0.23, 0.01)
PUFA 20:5	PhenoAge	-0.0990620	0.1523934	-0.2423985	0.0442745	952	-0.1 (-0.24, 0.04)
PUFA 20:5	GrimAge2	0.0243576	0.5005517	-0.0541565	0.1028716	952	0.02 (-0.05, 0.1)
PUFA 20:5	DNAmTL	0.0052814	0.0302415	0.0006307	0.0099321	952	0.01 (0, 0.01)
PUFA 20:5	YangCell	-0.0200980	0.0820648	-0.0433322	0.0031362	952	-0.02 (-0.04, 0)
PUFA 20:5	DunedinPoAm	0.0130701	0.1893185	-0.0077521	0.0338922	952	0.01 (-0.01, 0.03)
PUFA 22:5	Horvath1	-0.1190130	0.1313585	-0.2812325	0.0432064	952	-0.12 (-0.28, 0.04)
PUFA 22:5	Horvath2	-0.0799472	0.1594756	-0.1978681	0.0379737	952	-0.08 (-0.2, 0.04)
PUFA 22:5	Hannum	-0.1285312	0.0826593	-0.2774611	0.0203988	952	-0.13 (-0.28, 0.02)
PUFA 22:5	Lin	-0.0533735	0.5877246	-0.2681460	0.1613991	952	-0.05 (-0.27, 0.16)
PUFA 22:5	Zhang	-0.0286636	0.1515290	-0.0700399	0.0127126	952	-0.03 (-0.07, 0.01)
PUFA 22:5	VidalBralo	-0.0822317	0.2349208	-0.2283615	0.0638980	952	-0.08 (-0.23, 0.06)
PUFA 22:5	PhenoAge	-0.1307909	0.0465336	-0.2590903	-0.0024915	952	-0.13 (-0.26, 0)
PUFA 22:5	GrimAge2	-0.0348147	0.4819433	-0.1421907	0.0725613	952	-0.03 (-0.14, 0.07)
PUFA 22:5	DNAmTL	0.0046596	0.1031408	-0.0011524	0.0104716	952	0 (0, 0.01)
PUFA 22:5	YangCell	-0.0173310	0.1148774	-0.0397935	0.0051315	952	-0.02 (-0.04, 0.01)
PUFA 22:5	DunedinPoAm	0.0111027	0.2808144	-0.0107874	0.0329928	952	0.01 (-0.01, 0.03)
PUFA 22:6	Horvath1	-0.1866118	0.0302077	-0.3508963	-0.0223273	952	-0.19 (-0.35, -0.02)
PUFA 22:6	Horvath2	-0.1456529	0.0370789	-0.2804311	-0.0108746	952	-0.15 (-0.28, -0.01)
PUFA 22:6	Hannum	-0.2153477	0.0216654	-0.3910567	-0.0396386	952	-0.22 (-0.39, -0.04)
PUFA 22:6	Lin	-0.2420721	0.0249051	-0.4458448	-0.0382994	952	-0.24 (-0.45, -0.04)
PUFA 22:6	Zhang	-0.0500334	0.0433713	-0.0982157	-0.0018511	952	-0.05 (-0.1, 0)
PUFA 22:6	VidalBralo	-0.1343087	0.0264668	-0.2489513	-0.0196662	952	-0.13 (-0.25, -0.02)
PUFA 22:6	PhenoAge	-0.2243274	0.0090727	-0.3776039	-0.0710509	952	-0.22 (-0.38, -0.07)
PUFA 22:6	GrimAge2	-0.0171604	0.6302598	-0.0950730	0.0607522	952	-0.02 (-0.1, 0.06)
PUFA 22:6	DNAmTL	0.0036219	0.1328398	-0.0013365	0.0085803	952	0 (0, 0.01)
PUFA 22:6	YangCell	-0.0225811	0.0927874	-0.0497506	0.0045883	952	-0.02 (-0.05, 0)
PUFA 22:6	DunedinPoAm	0.0088727	0.3722995	-0.0125051	0.0302505	952	0.01 (-0.01, 0.03)

Stratified analyses - female

# dataframe to store results
subtypes_res_female = data.frame(nutrient = rep(c('SFA 4:0', 'SFA 6:0', 'SFA 8:0', 'SFA 10:0', 'SFA 12:0', 'SFA 14:0', 'SFA 16:0', 'SFA 18:0',
                            'MUFA 16:1', 'MUFA 18:1', 'MUFA 20:1', 'MUFA 22:1',
                            'PUFA 18:2', 'PUFA 20:4', 'PUFA 18:3', 'PUFA 20:5', 'PUFA 22:5', 'PUFA 22:6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 18),
  coef = NA, p = NA, CI_L = NA, CI_H = NA, n = NA)

# NHANES variable names
vars_ead = c("HorvathAge", "SkinBloodAge", "HannumAge", "LinAge", "ZhangAge", "VidalBraloAge", "PhenoAge", "GrimAge2Mort", "HorvathTelo", "YangCell_scale", "DunedinPoAm_scale")
subtypes = c("DRXTS040_log2", "DRXTS060_log2", "DRXTS080_log2", "DRXTS100_log2", "DRXTS120_log2", "DRXTS140_log2", "DRXTS160_log2", "DRXTS180_log2", "DRXTM161_log2", 
        "DRXTM181_log2", "DRXTM201_log2", "DRXTM221_log2", "DRXTP182_log2", "DRXTP204_log2", "DRXTP183_log2", "DRXTP205_log2", "DRXTP225_log2", "DRXTP226_log2")

# functions to fill in table
popTable1_subtypes_female = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = svyglm(formula(paste0(vars_ead[i], " ~ ", subtypes[j], " +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_female, 
            family = gaussian())
    tab[k,3] = summary(mod)$coef[2,1]
    tab[k,4] = summary(mod)$coef[2,4]
    tab[k,5] = confint(mod)[2,1]
    tab[k,6] = confint(mod)[2,2]
    tab[k,7] = nrow(mod$model)
    return(tab)
}

popTable2_subtypes_female = function(tab, subtypes, vars_ead){
    k = 1
    for (j in 1:length(subtypes)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_subtypes_female(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

subtypes_res_female = popTable2_subtypes_female(subtypes_res_female, subtypes, vars_ead)

subtypes_res_female$B_CI = paste0(round(subtypes_res_female$coef, 2), ' (', round(subtypes_res_female$CI_L, 2), ', ', round(subtypes_res_female$CI_H, 2), ')')

write.csv(subtypes_res_female, 'subtypes_res_female.csv')

nutrient	clock	coef	p	CI_L	CI_H	n	B_CI
SFA 4:0	Horvath1	0.0031797	0.9565221	-0.1251374	0.1314968	819	0 (-0.13, 0.13)
SFA 4:0	Horvath2	-0.0455195	0.2535261	-0.1299341	0.0388951	819	-0.05 (-0.13, 0.04)
SFA 4:0	Hannum	-0.0409499	0.6251425	-0.2240901	0.1421903	819	-0.04 (-0.22, 0.14)
SFA 4:0	Lin	-0.0897117	0.5160155	-0.3899084	0.2104850	819	-0.09 (-0.39, 0.21)
SFA 4:0	Zhang	-0.0197570	0.2994456	-0.0603483	0.0208343	819	-0.02 (-0.06, 0.02)
SFA 4:0	VidalBralo	0.0558844	0.6209345	-0.1909976	0.3027664	819	0.06 (-0.19, 0.3)
SFA 4:0	PhenoAge	-0.0251695	0.8329717	-0.2874318	0.2370929	819	-0.03 (-0.29, 0.24)
SFA 4:0	GrimAge2	0.0120055	0.8766115	-0.1580079	0.1820189	819	0.01 (-0.16, 0.18)
SFA 4:0	DNAmTL	0.0029160	0.2374814	-0.0022967	0.0081287	819	0 (0, 0.01)
SFA 4:0	YangCell	-0.0000634	0.9940101	-0.0186551	0.0185283	819	0 (-0.02, 0.02)
SFA 4:0	DunedinPoAm	0.0050418	0.7252093	-0.0264051	0.0364888	819	0.01 (-0.03, 0.04)
SFA 6:0	Horvath1	0.0070593	0.9031653	-0.1205548	0.1346734	819	0.01 (-0.12, 0.13)
SFA 6:0	Horvath2	-0.0414161	0.2265896	-0.1136007	0.0307684	819	-0.04 (-0.11, 0.03)
SFA 6:0	Hannum	0.0034898	0.9621552	-0.1583296	0.1653093	819	0 (-0.16, 0.17)
SFA 6:0	Lin	-0.0536190	0.5473169	-0.2476058	0.1403678	819	-0.05 (-0.25, 0.14)
SFA 6:0	Zhang	-0.0205127	0.2525014	-0.0584645	0.0174391	819	-0.02 (-0.06, 0.02)
SFA 6:0	VidalBralo	0.0796445	0.3862940	-0.1182337	0.2775228	819	0.08 (-0.12, 0.28)
SFA 6:0	PhenoAge	0.0338935	0.7305412	-0.1818958	0.2496828	819	0.03 (-0.18, 0.25)
SFA 6:0	GrimAge2	0.0385124	0.5746110	-0.1110835	0.1881083	819	0.04 (-0.11, 0.19)
SFA 6:0	DNAmTL	0.0006677	0.7764810	-0.0044952	0.0058307	819	0 (0, 0.01)
SFA 6:0	YangCell	0.0055091	0.5363769	-0.0138783	0.0248965	819	0.01 (-0.01, 0.02)
SFA 6:0	DunedinPoAm	0.0075557	0.5888249	-0.0229396	0.0380510	819	0.01 (-0.02, 0.04)
SFA 8:0	Horvath1	0.0068773	0.9253011	-0.1544758	0.1682303	819	0.01 (-0.15, 0.17)
SFA 8:0	Horvath2	-0.0615344	0.2813500	-0.1830005	0.0599317	819	-0.06 (-0.18, 0.06)
SFA 8:0	Hannum	-0.0779408	0.4362176	-0.2943310	0.1384495	819	-0.08 (-0.29, 0.14)
SFA 8:0	Lin	0.0047136	0.9620382	-0.2131772	0.2226045	819	0 (-0.21, 0.22)
SFA 8:0	Zhang	-0.0228786	0.3839635	-0.0794305	0.0336733	819	-0.02 (-0.08, 0.03)
SFA 8:0	VidalBralo	0.0714590	0.5211160	-0.1706661	0.3135841	819	0.07 (-0.17, 0.31)
SFA 8:0	PhenoAge	-0.0817271	0.4621924	-0.3224730	0.1590188	819	-0.08 (-0.32, 0.16)
SFA 8:0	GrimAge2	0.0157548	0.8242666	-0.1401288	0.1716383	819	0.02 (-0.14, 0.17)
SFA 8:0	DNAmTL	0.0022373	0.4433598	-0.0040755	0.0085502	819	0 (0, 0.01)
SFA 8:0	YangCell	-0.0181751	0.1618534	-0.0451542	0.0088039	819	-0.02 (-0.05, 0.01)
SFA 8:0	DunedinPoAm	0.0023558	0.8870931	-0.0341303	0.0388418	819	0 (-0.03, 0.04)
SFA 10:0	Horvath1	0.0222954	0.8093833	-0.1807288	0.2253197	819	0.02 (-0.18, 0.23)
SFA 10:0	Horvath2	-0.0845508	0.2750917	-0.2491410	0.0800395	819	-0.08 (-0.25, 0.08)
SFA 10:0	Hannum	-0.0629185	0.6025578	-0.3266851	0.2008482	819	-0.06 (-0.33, 0.2)
SFA 10:0	Lin	-0.1585118	0.2278580	-0.4356041	0.1185805	819	-0.16 (-0.44, 0.12)
SFA 10:0	Zhang	-0.0415873	0.2147188	-0.1120670	0.0288923	819	-0.04 (-0.11, 0.03)
SFA 10:0	VidalBralo	-0.0002433	0.9985375	-0.2923532	0.2918665	819	0 (-0.29, 0.29)
SFA 10:0	PhenoAge	-0.1181664	0.4093383	-0.4270838	0.1907510	819	-0.12 (-0.43, 0.19)
SFA 10:0	GrimAge2	-0.0229169	0.7976742	-0.2192351	0.1734014	819	-0.02 (-0.22, 0.17)
SFA 10:0	DNAmTL	0.0053101	0.1876321	-0.0031139	0.0137341	819	0.01 (0, 0.01)
SFA 10:0	YangCell	-0.0104105	0.5134024	-0.0450252	0.0242043	819	-0.01 (-0.05, 0.02)
SFA 10:0	DunedinPoAm	-0.0049519	0.8315712	-0.0561134	0.0462096	819	0 (-0.06, 0.05)
SFA 12:0	Horvath1	0.0512258	0.6113365	-0.1689136	0.2713653	819	0.05 (-0.17, 0.27)
SFA 12:0	Horvath2	-0.0757209	0.3988342	-0.2691226	0.1176808	819	-0.08 (-0.27, 0.12)
SFA 12:0	Hannum	-0.1327801	0.3896894	-0.4651480	0.1995879	819	-0.13 (-0.47, 0.2)
SFA 12:0	Lin	-0.0478759	0.7449772	-0.3707398	0.2749881	819	-0.05 (-0.37, 0.27)
SFA 12:0	Zhang	-0.0333224	0.3567885	-0.1109321	0.0442873	819	-0.03 (-0.11, 0.04)
SFA 12:0	VidalBralo	0.0686933	0.6334170	-0.2461334	0.3835201	819	0.07 (-0.25, 0.38)
SFA 12:0	PhenoAge	-0.0705780	0.6950017	-0.4648882	0.3237322	819	-0.07 (-0.46, 0.32)
SFA 12:0	GrimAge2	0.0835389	0.4684897	-0.1661519	0.3332298	819	0.08 (-0.17, 0.33)
SFA 12:0	DNAmTL	0.0042301	0.3214830	-0.0048911	0.0133512	819	0 (0, 0.01)
SFA 12:0	YangCell	-0.0453335	0.0217981	-0.0823724	-0.0082946	819	-0.05 (-0.08, -0.01)
SFA 12:0	DunedinPoAm	0.0077094	0.7978934	-0.0584069	0.0738257	819	0.01 (-0.06, 0.07)
SFA 14:0	Horvath1	0.0450671	0.7226371	-0.2332884	0.3234227	819	0.05 (-0.23, 0.32)
SFA 14:0	Horvath2	-0.1324055	0.3089452	-0.4101747	0.1453637	819	-0.13 (-0.41, 0.15)
SFA 14:0	Hannum	-0.0943960	0.6267028	-0.5185015	0.3297095	819	-0.09 (-0.52, 0.33)
SFA 14:0	Lin	-0.2854620	0.2095449	-0.7632961	0.1923720	819	-0.29 (-0.76, 0.19)
SFA 14:0	Zhang	-0.0606640	0.2605326	-0.1749616	0.0536336	819	-0.06 (-0.17, 0.05)
SFA 14:0	VidalBralo	0.0648332	0.7644810	-0.4100671	0.5397336	819	0.06 (-0.41, 0.54)
SFA 14:0	PhenoAge	-0.0840843	0.7377855	-0.6349078	0.4667392	819	-0.08 (-0.63, 0.47)
SFA 14:0	GrimAge2	0.0025899	0.9848350	-0.2972078	0.3023875	819	0 (-0.3, 0.3)
SFA 14:0	DNAmTL	0.0166398	0.0054356	0.0062869	0.0269926	819	0.02 (0.01, 0.03)
SFA 14:0	YangCell	-0.0454596	0.1057924	-0.1026675	0.0117483	819	-0.05 (-0.1, 0.01)
SFA 14:0	DunedinPoAm	0.0124939	0.7476777	-0.0727020	0.0976898	819	0.01 (-0.07, 0.1)
SFA 16:0	Horvath1	-0.1545142	0.7093069	-1.0628240	0.7537956	819	-0.15 (-1.06, 0.75)
SFA 16:0	Horvath2	-0.1618399	0.5763191	-0.7933533	0.4696734	819	-0.16 (-0.79, 0.47)
SFA 16:0	Hannum	-0.1127295	0.7983044	-1.0815279	0.8560689	819	-0.11 (-1.08, 0.86)
SFA 16:0	Lin	-0.0876689	0.8891552	-1.4709306	1.2955928	819	-0.09 (-1.47, 1.3)
SFA 16:0	Zhang	-0.0650119	0.5842074	-0.3241305	0.1941068	819	-0.07 (-0.32, 0.19)
SFA 16:0	VidalBralo	0.3172196	0.5323830	-0.7880269	1.4224661	819	0.32 (-0.79, 1.42)
SFA 16:0	PhenoAge	0.4462552	0.4885984	-0.9518464	1.8443567	819	0.45 (-0.95, 1.84)
SFA 16:0	GrimAge2	0.1381965	0.6317646	-0.4920595	0.7684525	819	0.14 (-0.49, 0.77)
SFA 16:0	DNAmTL	0.0048903	0.7683575	-0.0315519	0.0413326	819	0 (-0.03, 0.04)
SFA 16:0	YangCell	-0.0524548	0.4703614	-0.2099200	0.1050104	819	-0.05 (-0.21, 0.11)
SFA 16:0	DunedinPoAm	0.0459168	0.5147747	-0.1072673	0.1991010	819	0.05 (-0.11, 0.2)
SFA 18:0	Horvath1	-0.1526783	0.6608741	-0.9141465	0.6087899	819	-0.15 (-0.91, 0.61)
SFA 18:0	Horvath2	-0.1857728	0.4513536	-0.7195714	0.3480258	819	-0.19 (-0.72, 0.35)
SFA 18:0	Hannum	-0.1760226	0.6130667	-0.9362113	0.5841661	819	-0.18 (-0.94, 0.58)
SFA 18:0	Lin	-0.3566240	0.4533792	-1.3861001	0.6728521	819	-0.36 (-1.39, 0.67)
SFA 18:0	Zhang	-0.1149837	0.2544832	-0.3286813	0.0987140	819	-0.11 (-0.33, 0.1)
SFA 18:0	VidalBralo	0.4189578	0.3399660	-0.5216403	1.3595558	819	0.42 (-0.52, 1.36)
SFA 18:0	PhenoAge	0.4377124	0.4286298	-0.7569429	1.6323678	819	0.44 (-0.76, 1.63)
SFA 18:0	GrimAge2	0.1125317	0.6754939	-0.4759353	0.7009988	819	0.11 (-0.48, 0.7)
SFA 18:0	DNAmTL	0.0119176	0.3572248	-0.0158656	0.0397007	819	0.01 (-0.02, 0.04)
SFA 18:0	YangCell	-0.0320674	0.5670912	-0.1541752	0.0900404	819	-0.03 (-0.15, 0.09)
SFA 18:0	DunedinPoAm	0.0139174	0.8098066	-0.1131040	0.1409387	819	0.01 (-0.11, 0.14)
MUFA 16:1	Horvath1	0.1663707	0.4906314	-0.3573776	0.6901190	819	0.17 (-0.36, 0.69)
MUFA 16:1	Horvath2	0.1534065	0.4053268	-0.2440847	0.5508977	819	0.15 (-0.24, 0.55)
MUFA 16:1	Hannum	0.3071777	0.2483760	-0.2558371	0.8701926	819	0.31 (-0.26, 0.87)
MUFA 16:1	Lin	0.1254093	0.7537542	-0.7517221	1.0025408	819	0.13 (-0.75, 1)
MUFA 16:1	Zhang	0.0476546	0.5115731	-0.1100934	0.2054026	819	0.05 (-0.11, 0.21)
MUFA 16:1	VidalBralo	0.1127548	0.6979682	-0.5237626	0.7492723	819	0.11 (-0.52, 0.75)
MUFA 16:1	PhenoAge	0.6415851	0.0871807	-0.1146855	1.3978558	819	0.64 (-0.11, 1.4)
MUFA 16:1	GrimAge2	0.2029943	0.3147773	-0.2283397	0.6343283	819	0.2 (-0.23, 0.63)
MUFA 16:1	DNAmTL	-0.0039150	0.6799819	-0.0246950	0.0168651	819	0 (-0.02, 0.02)
MUFA 16:1	YangCell	-0.0185121	0.6923167	-0.1209777	0.0839535	819	-0.02 (-0.12, 0.08)
MUFA 16:1	DunedinPoAm	0.0707993	0.1099153	-0.0195126	0.1611111	819	0.07 (-0.02, 0.16)
MUFA 18:1	Horvath1	-0.5480270	0.2731935	-1.6103297	0.5142758	819	-0.55 (-1.61, 0.51)
MUFA 18:1	Horvath2	-0.1998205	0.4913248	-0.8299065	0.4302655	819	-0.2 (-0.83, 0.43)
MUFA 18:1	Hannum	-0.4027576	0.3588178	-1.3449690	0.5394538	819	-0.4 (-1.34, 0.54)
MUFA 18:1	Lin	-0.2180727	0.7070346	-1.4895066	1.0533612	819	-0.22 (-1.49, 1.05)
MUFA 18:1	Zhang	-0.1543172	0.2395180	-0.4314739	0.1228395	819	-0.15 (-0.43, 0.12)
MUFA 18:1	VidalBralo	0.2333815	0.6776286	-0.9957064	1.4624694	819	0.23 (-1, 1.46)
MUFA 18:1	PhenoAge	0.3009890	0.5774070	-0.8769155	1.4788936	819	0.3 (-0.88, 1.48)
MUFA 18:1	GrimAge2	0.2109546	0.5201020	-0.5020468	0.9239559	819	0.21 (-0.5, 0.92)
MUFA 18:1	DNAmTL	-0.0008018	0.9603219	-0.0362600	0.0346564	819	0 (-0.04, 0.03)
MUFA 18:1	YangCell	-0.0312622	0.6295244	-0.1728920	0.1103676	819	-0.03 (-0.17, 0.11)
MUFA 18:1	DunedinPoAm	0.0434694	0.5537142	-0.1163954	0.2033341	819	0.04 (-0.12, 0.2)
MUFA 20:1	Horvath1	-0.2507922	0.2011915	-0.6621955	0.1606111	819	-0.25 (-0.66, 0.16)
MUFA 20:1	Horvath2	-0.0551505	0.6201240	-0.2982179	0.1879169	819	-0.06 (-0.3, 0.19)
MUFA 20:1	Hannum	-0.1919567	0.3322562	-0.6157251	0.2318117	819	-0.19 (-0.62, 0.23)
MUFA 20:1	Lin	-0.2209218	0.2454920	-0.6231815	0.1813379	819	-0.22 (-0.62, 0.18)
MUFA 20:1	Zhang	-0.0696602	0.2294379	-0.1918823	0.0525619	819	-0.07 (-0.19, 0.05)
MUFA 20:1	VidalBralo	-0.1234548	0.4183768	-0.4527566	0.2058470	819	-0.12 (-0.45, 0.21)
MUFA 20:1	PhenoAge	-0.1491048	0.4248823	-0.5526525	0.2544428	819	-0.15 (-0.55, 0.25)
MUFA 20:1	GrimAge2	0.1006861	0.3485584	-0.1296418	0.3310140	819	0.1 (-0.13, 0.33)
MUFA 20:1	DNAmTL	-0.0010291	0.8564963	-0.0135388	0.0114806	819	0 (-0.01, 0.01)
MUFA 20:1	YangCell	-0.0278628	0.4526768	-0.1081662	0.0524405	819	-0.03 (-0.11, 0.05)
MUFA 20:1	DunedinPoAm	0.0213384	0.4363904	-0.0379274	0.0806042	819	0.02 (-0.04, 0.08)
MUFA 22:1	Horvath1	0.0551332	0.5197426	-0.1310462	0.2413126	819	0.06 (-0.13, 0.24)
MUFA 22:1	Horvath2	0.0309766	0.4205362	-0.0520493	0.1140024	819	0.03 (-0.05, 0.11)
MUFA 22:1	Hannum	-0.0409185	0.5819349	-0.2030099	0.1211729	819	-0.04 (-0.2, 0.12)
MUFA 22:1	Lin	-0.0779097	0.4180245	-0.2855616	0.1297423	819	-0.08 (-0.29, 0.13)
MUFA 22:1	Zhang	-0.0078492	0.6797421	-0.0494785	0.0337800	819	-0.01 (-0.05, 0.03)
MUFA 22:1	VidalBralo	-0.0562065	0.4017435	-0.2006921	0.0882791	819	-0.06 (-0.2, 0.09)
MUFA 22:1	PhenoAge	0.1045858	0.1425646	-0.0426586	0.2518302	819	0.1 (-0.04, 0.25)
MUFA 22:1	GrimAge2	-0.0204662	0.6132877	-0.1089096	0.0679771	819	-0.02 (-0.11, 0.07)
MUFA 22:1	DNAmTL	-0.0002095	0.9238792	-0.0050329	0.0046139	819	0 (-0.01, 0)
MUFA 22:1	YangCell	-0.0054834	0.5639577	-0.0261924	0.0152256	819	-0.01 (-0.03, 0.02)
MUFA 22:1	DunedinPoAm	0.0065301	0.5150807	-0.0152713	0.0283315	819	0.01 (-0.02, 0.03)
PUFA 18:2	Horvath1	-0.6118294	0.1297419	-1.4417963	0.2181374	819	-0.61 (-1.44, 0.22)
PUFA 18:2	Horvath2	-0.1291773	0.6320083	-0.7187272	0.4603727	819	-0.13 (-0.72, 0.46)
PUFA 18:2	Hannum	-0.7910295	0.0496535	-1.5805740	-0.0014851	819	-0.79 (-1.58, 0)
PUFA 18:2	Lin	-0.3515342	0.4581123	-1.3773849	0.6743165	819	-0.35 (-1.38, 0.67)
PUFA 18:2	Zhang	-0.0697322	0.4738511	-0.2807687	0.1413043	819	-0.07 (-0.28, 0.14)
PUFA 18:2	VidalBralo	-0.2950342	0.4447677	-1.1301633	0.5400950	819	-0.3 (-1.13, 0.54)
PUFA 18:2	PhenoAge	-0.6066995	0.1397727	-1.4541052	0.2407062	819	-0.61 (-1.45, 0.24)
PUFA 18:2	GrimAge2	-0.1871722	0.4697812	-0.7482925	0.3739480	819	-0.19 (-0.75, 0.37)
PUFA 18:2	DNAmTL	-0.0058612	0.6716939	-0.0361312	0.0244088	819	-0.01 (-0.04, 0.02)
PUFA 18:2	YangCell	-0.0323131	0.5526469	-0.1508234	0.0861972	819	-0.03 (-0.15, 0.09)
PUFA 18:2	DunedinPoAm	-0.0139453	0.8224563	-0.1504904	0.1225999	819	-0.01 (-0.15, 0.12)
PUFA 20:4	Horvath1	-0.0691337	0.5501429	-0.3210629	0.1827954	819	-0.07 (-0.32, 0.18)
PUFA 20:4	Horvath2	-0.0121885	0.8649714	-0.1697663	0.1453892	819	-0.01 (-0.17, 0.15)
PUFA 20:4	Hannum	-0.0125429	0.9240942	-0.3021278	0.2770420	819	-0.01 (-0.3, 0.28)
PUFA 20:4	Lin	-0.0148429	0.9383707	-0.4371672	0.4074815	819	-0.01 (-0.44, 0.41)
PUFA 20:4	Zhang	-0.0306474	0.3938447	-0.1080678	0.0467730	819	-0.03 (-0.11, 0.05)
PUFA 20:4	VidalBralo	-0.1423673	0.1968502	-0.3734416	0.0887070	819	-0.14 (-0.37, 0.09)
PUFA 20:4	PhenoAge	0.0402656	0.8024705	-0.3132628	0.3937939	819	0.04 (-0.31, 0.39)
PUFA 20:4	GrimAge2	-0.0008343	0.9895456	-0.1409302	0.1392616	819	0 (-0.14, 0.14)
PUFA 20:4	DNAmTL	0.0012000	0.7874824	-0.0085735	0.0109735	819	0 (-0.01, 0.01)
PUFA 20:4	YangCell	-0.0142144	0.4330948	-0.0534017	0.0249728	819	-0.01 (-0.05, 0.02)
PUFA 20:4	DunedinPoAm	0.0152790	0.3652512	-0.0209707	0.0515286	819	0.02 (-0.02, 0.05)
PUFA 18:3	Horvath1	-0.5203228	0.0725176	-1.0991398	0.0584943	819	-0.52 (-1.1, 0.06)
PUFA 18:3	Horvath2	-0.2291530	0.3121157	-0.7132392	0.2549333	819	-0.23 (-0.71, 0.25)
PUFA 18:3	Hannum	-0.9681645	0.0127490	-1.6749557	-0.2613732	819	-0.97 (-1.67, -0.26)
PUFA 18:3	Lin	-0.3937308	0.2947193	-1.1942807	0.4068190	819	-0.39 (-1.19, 0.41)
PUFA 18:3	Zhang	-0.1332215	0.1323901	-0.3153602	0.0489171	819	-0.13 (-0.32, 0.05)
PUFA 18:3	VidalBralo	-0.2964534	0.3425284	-0.9657501	0.3728434	819	-0.3 (-0.97, 0.37)
PUFA 18:3	PhenoAge	-0.7275194	0.0679568	-1.5211603	0.0661215	819	-0.73 (-1.52, 0.07)
PUFA 18:3	GrimAge2	-0.3617735	0.1547409	-0.8886027	0.1650557	819	-0.36 (-0.89, 0.17)
PUFA 18:3	DNAmTL	0.0132995	0.2703522	-0.0123171	0.0389160	819	0.01 (-0.01, 0.04)
PUFA 18:3	YangCell	-0.0032620	0.9453207	-0.1078980	0.1013741	819	0 (-0.11, 0.1)
PUFA 18:3	DunedinPoAm	-0.0497276	0.3891190	-0.1740468	0.0745916	819	-0.05 (-0.17, 0.07)
PUFA 20:5	Horvath1	-0.0445698	0.4655914	-0.1768946	0.0877549	819	-0.04 (-0.18, 0.09)
PUFA 20:5	Horvath2	0.0139364	0.7312765	-0.0750465	0.1029192	819	0.01 (-0.08, 0.1)
PUFA 20:5	Hannum	-0.0839367	0.2282721	-0.2308073	0.0629340	819	-0.08 (-0.23, 0.06)
PUFA 20:5	Lin	-0.0089603	0.9172897	-0.1987482	0.1808276	819	-0.01 (-0.2, 0.18)
PUFA 20:5	Zhang	-0.0172004	0.3047169	-0.0529510	0.0185503	819	-0.02 (-0.05, 0.02)
PUFA 20:5	VidalBralo	-0.0490138	0.5019543	-0.2075369	0.1095093	819	-0.05 (-0.21, 0.11)
PUFA 20:5	PhenoAge	-0.1563489	0.1265581	-0.3664327	0.0537348	819	-0.16 (-0.37, 0.05)
PUFA 20:5	GrimAge2	-0.0287048	0.5244708	-0.1267727	0.0693630	819	-0.03 (-0.13, 0.07)
PUFA 20:5	DNAmTL	0.0036111	0.1384960	-0.0014143	0.0086364	819	0 (0, 0.01)
PUFA 20:5	YangCell	-0.0030191	0.7247389	-0.0218158	0.0157777	819	0 (-0.02, 0.02)
PUFA 20:5	DunedinPoAm	-0.0027283	0.8222130	-0.0294048	0.0239483	819	0 (-0.03, 0.02)
PUFA 22:5	Horvath1	-0.0891261	0.1477652	-0.2164489	0.0381968	819	-0.09 (-0.22, 0.04)
PUFA 22:5	Horvath2	-0.0202685	0.5140728	-0.0877710	0.0472341	819	-0.02 (-0.09, 0.05)
PUFA 22:5	Hannum	-0.0974194	0.0721437	-0.2056204	0.0107817	819	-0.1 (-0.21, 0.01)
PUFA 22:5	Lin	-0.0330929	0.6859779	-0.2123294	0.1461436	819	-0.03 (-0.21, 0.15)
PUFA 22:5	Zhang	-0.0259287	0.0978722	-0.0576916	0.0058342	819	-0.03 (-0.06, 0.01)
PUFA 22:5	VidalBralo	-0.0581525	0.3557181	-0.1932768	0.0769717	819	-0.06 (-0.19, 0.08)
PUFA 22:5	PhenoAge	-0.1272725	0.1723175	-0.3214606	0.0669156	819	-0.13 (-0.32, 0.07)
PUFA 22:5	GrimAge2	-0.0533608	0.1952213	-0.1396236	0.0329020	819	-0.05 (-0.14, 0.03)
PUFA 22:5	DNAmTL	0.0027769	0.2706324	-0.0025751	0.0081288	819	0 (0, 0.01)
PUFA 22:5	YangCell	0.0010109	0.9115873	-0.0190134	0.0210352	819	0 (-0.02, 0.02)
PUFA 22:5	DunedinPoAm	-0.0045515	0.7178365	-0.0321607	0.0230577	819	0 (-0.03, 0.02)
PUFA 22:6	Horvath1	-0.1150422	0.0692479	-0.2412473	0.0111629	819	-0.12 (-0.24, 0.01)
PUFA 22:6	Horvath2	-0.0599459	0.1137774	-0.1373700	0.0174781	819	-0.06 (-0.14, 0.02)
PUFA 22:6	Hannum	-0.1435690	0.0334479	-0.2730910	-0.0140471	819	-0.14 (-0.27, -0.01)
PUFA 22:6	Lin	0.0032638	0.9736114	-0.2138224	0.2203499	819	0 (-0.21, 0.22)
PUFA 22:6	Zhang	-0.0283363	0.1252265	-0.0662587	0.0095861	819	-0.03 (-0.07, 0.01)
PUFA 22:6	VidalBralo	-0.0637486	0.3793112	-0.2197229	0.0922256	819	-0.06 (-0.22, 0.09)
PUFA 22:6	PhenoAge	-0.1724784	0.0946122	-0.3813599	0.0364030	819	-0.17 (-0.38, 0.04)
PUFA 22:6	GrimAge2	-0.0438902	0.2992228	-0.1340197	0.0462393	819	-0.04 (-0.13, 0.05)
PUFA 22:6	DNAmTL	0.0030263	0.2070650	-0.0020093	0.0080618	819	0 (0, 0.01)
PUFA 22:6	YangCell	-0.0113672	0.2379697	-0.0317102	0.0089758	819	-0.01 (-0.03, 0.01)
PUFA 22:6	DunedinPoAm	0.0006543	0.9532093	-0.0238800	0.0251887	819	0 (-0.02, 0.03)

Sensitivity analyses with imputed covatiates

load("nhanes_fattyAcids_clean_data.RData")
set.seed(123)

df_use = df[df$use_fat,]

# check that covariates are coded as numbers
table(df_use$RIAGENDR)

table(df_use$RIDRETH1)

table(df_use$active_modvig)
#   No  Yes 
# 1197 1022
df_use$active_modvig = ifelse(df_use$active_modvig == 'No', 0,
                        ifelse(df_use$active_modvig == 'Yes', 1, NA))
table(df_use$active_modvig)
#    0    1 
# 1197 1022 

table(df_use$SMOKE_3cat)
#   Never  Former Current 
#     1004     863     349  
df_use$SMOKE_3cat <- ifelse(df_use$SMOKE_3cat == "Never", 0, 
                        ifelse(df_use$SMOKE_3cat == "Former", 1, 
                        ifelse(df_use$SMOKE_3cat == "Current", 2, NA)))
table(df_use$SMOKE_3cat)
#    0    1    2 
# 1004  863  349 

table(df_use$EDUCATION)
# High School Diploma (including GED)               Less Than High School               More Than High School 
#                                455                                 995                                 770 
df_use$EDUCATION <- ifelse(df_use$EDUCATION == "Less Than High School", 0, 
                        ifelse(df_use$EDUCATION == "High School Diploma (including GED)", 1, 
                        ifelse(df_use$EDUCATION == "More Than High School", 2, NA)))
table(df_use$EDUCATION)     
#    0    1    2 
#   995 455 770  

table(df_use$OCCUPATION)
#   hiblue  hiwhite  lowblue lowwhite   nowork 
#       301      505      857      376       55 
df_use$OCCUPATION <- ifelse(df_use$OCCUPATION == "lowblue", 0, ifelse(df_use$OCCUPATION == "hiblue", 1, 
                ifelse(df_use$OCCUPATION == "lowwhite", 2, ifelse(df_use$OCCUPATION == "hiwhite", 3, 
                                                       ifelse(df_use$OCCUPATION == "nowork", 4, NA)))))
table(df_use$OCCUPATION)
#   0   1   2   3   4 
# 857 301 376 505  55  

df_use$RIAGENDR <- as.factor(df_use$RIAGENDR)
df_use$RIDRETH1 <- as.factor(df_use$RIDRETH1)
df_use$EDUCATION <- as.factor(df_use$EDUCATION)
df_use$OCCUPATION <- as.factor(df_use$OCCUPATION)
df_use$SMOKE_3cat <- as.factor(df_use$SMOKE_3cat)
df_use$INDFMPIR <- as.numeric(df_use$INDFMPIR)   
df_use$active_modvig <- as.numeric(df_use$active_modvig)

# subset dataset to just covariates
# mice will try to impute any missing value so will rejoin imputed covariates with the exposures/outcomes after
subs <- df_use %>% 
  dplyr::select(RIDAGEYR, RIAGENDR, RIDRETH1, BMXBMI, DRXTKCAL, active_modvig, EDUCATION, OCCUPATION, INDFMPIR, drinkvol, SMOKE_3cat)
non_subs <- df_use %>% 
  dplyr::select(-RIDAGEYR, -RIAGENDR, -RIDRETH1, -BMXBMI, -DRXTKCAL, -active_modvig, -EDUCATION, -OCCUPATION, -INDFMPIR, -drinkvol, -SMOKE_3cat)

df_imp <- mice(subs, m = 5)              # imputation with 5 iterations 
df_imp <- mice::cbind(df_imp, non_subs)  # rejoin exposure/outcome data

# output is 5 iterations of complete datasets, each iteration can be accessed with the complete function
table(df_use$OCCUPATION)                            # original non-imputer values
#   0   1   2   3   4 
# 857 301 376 505  55
table(complete(df_imp, action = 2)$OCCUPATION)  # after imputation from the 2nd iteration
#   0   1   2   3   4 
# 928 310 399 524  59 


# create imputation list compatible with survey design
l <- list((complete(df_imp, action = 1)), (complete(df_imp, action = 2)), (complete(df_imp, action = 3)), 
          (complete(df_imp, action = 4)), (complete(df_imp, action = 5)))
df_imp_list <- imputationList(l) 

# specify survey design
nhanesDesign_impute <- svydesign(id = ~SDMVPSU,  # Primary Sampling Units (PSU)
                          strata  = ~SDMVSTRA, # Stratification used in the survey
                          weights = ~WTDN4YR,   # Survey weights (using those from DNAm dataset)
                          nest    = TRUE,      # Whether PSUs are nested within strata
                          data    = df_imp_list)  # only change from non-imputed version

dim(nhanesDesign_impute)
# 2220  288    5

Associations with fatty acid intake

fat_res_alcSmkAct_imp = data.frame(nutrient = rep(c('Total fat', 'Saturated fat', 'Monounsaturated fat', 'Polyunsaturated fat', 'P:S ratio', 'Omega-3', 'Omega-6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 7),
  coef = NA, p = NA, CI_L = NA, CI_H = NA)

# NHANES variable names
# vars_fat = c('DRXTTFAT', 'DRXTSFAT', 'DRXTMFAT', 'DRXTPFAT', 'psatfatRatio', 'psatfatRatioGt1', 'omega3', 'omega6', 'omegaRatio', 'omegaRatio5')
vars_fat = c('DRXTTFAT_log2', 'DRXTSFAT_log2', 'DRXTMFAT_log2', 'DRXTPFAT_log2', 'psatfatRatio', 'omega3_log2', 'omega6_log2')
vars_ead = c('HorvathAge', 'SkinBloodAge', 'HannumAge', 'LinAge', 'ZhangAge', 'VidalBraloAge', 'PhenoAge', 'GrimAge2Mort', 'HorvathTelo', 'YangCell_scale', 'DunedinPoAm_scale')


# functions to fill in table
popTable1 = function(tab, i, j, k){
    # i = EAD variable
    # j = OCM variable
    # k = table row
    mod <- with(nhanesDesign_impute, svyglm(formula(paste0(vars_ead[i], " ~ ", vars_fat[j], " +
        RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
        BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
        factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
                        subset = use,
                        design = nhanesDesign_impute, 
                        family = gaussian()))
    tab[k,3] = summary(pool(mod), conf.int = TRUE)[2,2]
    tab[k,4] = summary(pool(mod), conf.int = TRUE)[2,6]
    tab[k,5] = summary(pool(mod), conf.int = TRUE)[2,7]
    tab[k,6] = summary(pool(mod), conf.int = TRUE)[2,8]
    return(tab)
}

popTable2 = function(tab, vars_fat, vars_ead){
    k = 1
    for (j in 1:length(vars_fat)){
        for (i in 1:length(vars_ead)){
            tab = popTable1(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

fat_res_alcSmkAct_imp = popTable2(fat_res_alcSmkAct_imp, vars_fat, vars_ead)

fat_res_alcSmkAct_imp$B_CI = paste0(round(fat_res_alcSmkAct_imp$coef, 2), ' ', fat_res_alcSmkAct_imp$CI)

write.csv(fat_res_alcSmkAct_imp, 'fat_res_alcSmkAct_imp.csv')

nutrient	clock	coef	p	CI_L	CI_H	B_CI
Total fat	Horvath1	-0.4981129	0.1479306	-1.2264653	0.2302395	-0.5
Total fat	Horvath2	-0.1853722	0.4489085	-0.7377512	0.3670067	-0.19
Total fat	Hannum	-0.2333921	0.5202538	-1.0575450	0.5907609	-0.23
Total fat	Lin	-0.4768704	0.3756668	-1.6813391	0.7275982	-0.48
Total fat	Zhang	-0.0421933	0.6578074	-0.2605532	0.1761666	-0.04
Total fat	VidalBralo	0.1122775	0.8046684	-0.9334909	1.1580460	0.11
Total fat	PhenoAge	-0.2231356	0.6054042	-1.2097407	0.7634694	-0.22
Total fat	GrimAge2	0.3565550	0.1610589	-0.1847268	0.8978367	0.36
Total fat	DNAmTL	0.0059993	0.6185851	-0.0215502	0.0335487	0.01
Total fat	YangCell	-0.0595173	0.2983791	-0.1859133	0.0668788	-0.06
Total fat	DunedinPoAm	0.0482930	0.4223244	-0.0870652	0.1836512	0.05
Saturated fat	Horvath1	0.0361469	0.8454274	-0.3912918	0.4635856	0.04
Saturated fat	Horvath2	0.0962034	0.6035043	-0.3269645	0.5193713	0.1
Saturated fat	Hannum	0.1323423	0.5837411	-0.4186580	0.6833427	0.13
Saturated fat	Lin	-0.3285733	0.3044414	-1.0366193	0.3794727	-0.33
Saturated fat	Zhang	0.0318135	0.6089386	-0.1103621	0.1739890	0.03
Saturated fat	VidalBralo	0.3262868	0.2838384	-0.3441991	0.9967727	0.33
Saturated fat	PhenoAge	0.2314511	0.4418778	-0.4469483	0.9098505	0.23
Saturated fat	GrimAge2	0.3945685	0.0626691	-0.0278625	0.8169995	0.39
Saturated fat	DNAmTL	0.0055647	0.5111359	-0.0136512	0.0247806	0.01
Saturated fat	YangCell	-0.0688413	0.1050657	-0.1566600	0.0189774	-0.07
Saturated fat	DunedinPoAm	0.0518336	0.2693270	-0.0511954	0.1548626	0.05
Monounsaturated fat	Horvath1	-0.4241555	0.1636670	-1.0726574	0.2243464	-0.42
Monounsaturated fat	Horvath2	-0.1184567	0.5590911	-0.5803350	0.3434216	-0.12
Monounsaturated fat	Hannum	0.0006782	0.9983329	-0.7487251	0.7500815	0
Monounsaturated fat	Lin	-0.2535323	0.5802141	-1.2996237	0.7925591	-0.25
Monounsaturated fat	Zhang	-0.0323304	0.7052557	-0.2286488	0.1639880	-0.03
Monounsaturated fat	VidalBralo	0.2290421	0.5603497	-0.6667007	1.1247849	0.23
Monounsaturated fat	PhenoAge	0.1460282	0.7069830	-0.7458665	1.0379229	0.15
Monounsaturated fat	GrimAge2	0.4025665	0.0741681	-0.0522967	0.8574297	0.4
Monounsaturated fat	DNAmTL	-0.0074008	0.4795405	-0.0310827	0.0162811	-0.01
Monounsaturated fat	YangCell	-0.0401722	0.4590374	-0.1626748	0.0823304	-0.04
Monounsaturated fat	DunedinPoAm	0.0811453	0.1016481	-0.0211180	0.1834085	0.08
Polyunsaturated fat	Horvath1	-0.6049171	0.0288517	-1.1250104	-0.0848238	-0.6
Polyunsaturated fat	Horvath2	-0.3730450	0.0645517	-0.7761637	0.0300736	-0.37
Polyunsaturated fat	Hannum	-0.5307646	0.0427309	-1.0381031	-0.0234261	-0.53
Polyunsaturated fat	Lin	-0.4618855	0.1554923	-1.1522812	0.2285103	-0.46
Polyunsaturated fat	Zhang	-0.0940464	0.1541407	-0.2340672	0.0459743	-0.09
Polyunsaturated fat	VidalBralo	-0.2643938	0.3063550	-0.8360315	0.3072440	-0.26
Polyunsaturated fat	PhenoAge	-0.5668436	0.0468119	-1.1229315	-0.0107556	-0.57
Polyunsaturated fat	GrimAge2	-0.1618346	0.3301122	-0.5309149	0.2072458	-0.16
Polyunsaturated fat	DNAmTL	0.0045841	0.5666466	-0.0136427	0.0228109	0
Polyunsaturated fat	YangCell	-0.0219324	0.5301049	-0.1013177	0.0574529	-0.02
Polyunsaturated fat	DunedinPoAm	-0.0178408	0.6377342	-0.1045402	0.0688586	-0.02
P:S ratio	Horvath1	-0.8337459	0.0158605	-1.4527995	-0.2146923	-0.83
P:S ratio	Horvath2	-0.4916596	0.1009825	-1.1097677	0.1264485	-0.49
P:S ratio	Hannum	-0.7241872	0.0524248	-1.4585599	0.0101855	-0.72
P:S ratio	Lin	-0.2263061	0.5383285	-1.0625267	0.6099144	-0.23
P:S ratio	Zhang	-0.1374630	0.0880777	-0.3020709	0.0271449	-0.14
P:S ratio	VidalBralo	-0.6955251	0.0547428	-1.4099967	0.0189465	-0.7
P:S ratio	PhenoAge	-0.9726576	0.0294962	-1.8136596	-0.1316557	-0.97
P:S ratio	GrimAge2	-0.5528632	0.0560085	-1.1247126	0.0189863	-0.55
P:S ratio	DNAmTL	0.0051794	0.6818424	-0.0237937	0.0341524	0.01
P:S ratio	YangCell	0.0376342	0.4180842	-0.0668452	0.1421137	0.04
P:S ratio	DunedinPoAm	-0.0762505	0.0997451	-0.1716865	0.0191856	-0.08
Omega-3	Horvath1	-0.6052002	0.0190213	-1.0742934	-0.1361071	-0.61
Omega-3	Horvath2	-0.3702007	0.0431790	-0.7251013	-0.0153000	-0.37
Omega-3	Hannum	-0.6560135	0.0201857	-1.1718363	-0.1401907	-0.66
Omega-3	Lin	-0.5166830	0.1126115	-1.1933131	0.1599470	-0.52
Omega-3	Zhang	-0.1153983	0.0674424	-0.2418437	0.0110471	-0.12
Omega-3	VidalBralo	-0.4732572	0.0636959	-0.9825179	0.0360035	-0.47
Omega-3	PhenoAge	-0.8486458	0.0156228	-1.4765252	-0.2207663	-0.85
Omega-3	GrimAge2	-0.3320346	0.0749389	-0.7084835	0.0444144	-0.33
Omega-3	DNAmTL	0.0185979	0.0329136	0.0020461	0.0351498	0.02
Omega-3	YangCell	-0.0268583	0.4051836	-0.0992771	0.0455604	-0.03
Omega-3	DunedinPoAm	-0.0136658	0.7002093	-0.0951204	0.0677888	-0.01
Omega-6	Horvath1	-0.5310397	0.0549375	-1.0771268	0.0150474	-0.53
Omega-6	Horvath2	-0.3353396	0.1036234	-0.7609622	0.0902830	-0.34
Omega-6	Hannum	-0.4597949	0.0756897	-0.9827919	0.0632022	-0.46
Omega-6	Lin	-0.3874250	0.2487073	-1.1215906	0.3467405	-0.39
Omega-6	Zhang	-0.0792380	0.2280516	-0.2221740	0.0636980	-0.08
Omega-6	VidalBralo	-0.2047954	0.4212612	-0.7773763	0.3677855	-0.2
Omega-6	PhenoAge	-0.4592560	0.1052350	-1.0454892	0.1269771	-0.46
Omega-6	GrimAge2	-0.1115091	0.4949990	-0.4817819	0.2587637	-0.11
Omega-6	DNAmTL	0.0032414	0.6661047	-0.0139720	0.0204548	0
Omega-6	YangCell	-0.0176734	0.5945400	-0.0934733	0.0581265	-0.02
Omega-6	DunedinPoAm	-0.0124869	0.7397319	-0.0988895	0.0739156	-0.01

Associations with individual fatty acids

# without scaling epigenetic age variables
vars_ead = c("HorvathAge", "SkinBloodAge", "HannumAge", "LinAge", "ZhangAge", "VidalBraloAge", "PhenoAge", "GrimAge2Mort", "HorvathTelo", "YangCell_scale", "DunedinPoAm_scale")
subtypes = c("DRXTS040_log2", "DRXTS060_log2", "DRXTS080_log2", "DRXTS100_log2", "DRXTS120_log2", "DRXTS140_log2", "DRXTS160_log2", "DRXTS180_log2", "DRXTM161_log2", 
        "DRXTM181_log2", "DRXTM201_log2", "DRXTM221_log2", "DRXTP182_log2", "DRXTP204_log2", "DRXTP183_log2", "DRXTP205_log2", "DRXTP225_log2", "DRXTP226_log2")

subtypes_res_imp = data.frame(nutrient = rep(c('SFA 4:0', 'SFA 6:0', 'SFA 8:0', 'SFA 10:0', 'SFA 12:0', 'SFA 14:0', 'SFA 16:0', 'SFA 18:0',
                            'MUFA 16:1', 'MUFA 18:1', 'MUFA 20:1', 'MUFA 22:1',
                            'PUFA 18:2', 'PUFA 20:4', 'PUFA 18:3', 'PUFA 20:5', 'PUFA 22:5', 'PUFA 22:6'), each = 11),
  clock = rep(c('Horvath1', 'Horvath2', 'Hannum', 'Lin', 'Zhang', 'VidalBralo', 'PhenoAge', 'GrimAge2', 
        'DNAmTL', 'YangCell', 'DunedinPoAm'), times = 18),
  coef = NA, p = NA, CI_L = NA, CI_H = NA)

# functions to fill in table
popTable1_subtypes_imp = function(tab, i, j, k){
    # i = EAD variable
    # j = fat variable
    # k = table row
    mod = with(nhanesDesign_impute, svyglm(formula(paste0(vars_ead[i], " ~ ", subtypes[j], " +
            RIDAGEYR + I(RIDAGEYR^2) + factor(RIAGENDR) + factor(RIDRETH1) +
            BMXBMI + DRXTKCAL + factor(SMOKE_3cat) + drinkvol + factor(active_modvig) +
            factor(EDUCATION) + factor(OCCUPATION) + INDFMPIR")),
            design = nhanesDesign_use, 
            family = gaussian()))
    tab[k,3] = summary(pool(mod), conf.int = TRUE)[2,2]
    tab[k,4] = summary(pool(mod), conf.int = TRUE)[2,6]
    tab[k,5] = summary(pool(mod), conf.int = TRUE)[2,7]
    tab[k,6] = summary(pool(mod), conf.int = TRUE)[2,8]
    return(tab)
}

popTable2_subtypes_imp = function(tab, subtypes, vars_ead){
    k = 1
    for (j in 1:length(subtypes)){
        for (i in 1:length(vars_ead)){
            tab = popTable1_subtypes_imp(tab, i, j, k)
            k = k + 1
        }
    }
    return(tab)
}

subtypes_res_imp = popTable2_subtypes_imp(subtypes_res_imp, subtypes, vars_ead)

subtypes_res_imp$B_CI = paste0(round(subtypes_res_imp$coef, 2), ' (', round(subtypes_res_imp$CI_L, 2), ', ', round(subtypes_res_imp$CI_H, 2), ')')

write.csv(subtypes_res_imp, 'subtypes_res_noScale_imp.csv')

nutrient	clock	coef	p	CI_L	CI_H
SFA 4:0	Horvath1	NA	NA	NA	NA
SFA 4:0	Horvath2	NA	NA	NA	NA
SFA 4:0	Hannum	NA	NA	NA	NA
SFA 4:0	Lin	NA	NA	NA	NA
SFA 4:0	Zhang	NA	NA	NA	NA
SFA 4:0	VidalBralo	NA	NA	NA	NA
SFA 4:0	PhenoAge	NA	NA	NA	NA
SFA 4:0	GrimAge2	NA	NA	NA	NA
SFA 4:0	DNAmTL	NA	NA	NA	NA
SFA 4:0	YangCell	NA	NA	NA	NA
SFA 4:0	DunedinPoAm	NA	NA	NA	NA
SFA 6:0	Horvath1	NA	NA	NA	NA
SFA 6:0	Horvath2	NA	NA	NA	NA
SFA 6:0	Hannum	NA	NA	NA	NA
SFA 6:0	Lin	NA	NA	NA	NA
SFA 6:0	Zhang	NA	NA	NA	NA
SFA 6:0	VidalBralo	NA	NA	NA	NA
SFA 6:0	PhenoAge	NA	NA	NA	NA
SFA 6:0	GrimAge2	NA	NA	NA	NA
SFA 6:0	DNAmTL	NA	NA	NA	NA
SFA 6:0	YangCell	NA	NA	NA	NA
SFA 6:0	DunedinPoAm	NA	NA	NA	NA
SFA 8:0	Horvath1	NA	NA	NA	NA
SFA 8:0	Horvath2	NA	NA	NA	NA
SFA 8:0	Hannum	NA	NA	NA	NA
SFA 8:0	Lin	NA	NA	NA	NA
SFA 8:0	Zhang	NA	NA	NA	NA
SFA 8:0	VidalBralo	NA	NA	NA	NA
SFA 8:0	PhenoAge	NA	NA	NA	NA
SFA 8:0	GrimAge2	NA	NA	NA	NA
SFA 8:0	DNAmTL	NA	NA	NA	NA
SFA 8:0	YangCell	NA	NA	NA	NA
SFA 8:0	DunedinPoAm	NA	NA	NA	NA
SFA 10:0	Horvath1	NA	NA	NA	NA
SFA 10:0	Horvath2	NA	NA	NA	NA
SFA 10:0	Hannum	NA	NA	NA	NA
SFA 10:0	Lin	NA	NA	NA	NA
SFA 10:0	Zhang	NA	NA	NA	NA
SFA 10:0	VidalBralo	NA	NA	NA	NA
SFA 10:0	PhenoAge	NA	NA	NA	NA
SFA 10:0	GrimAge2	NA	NA	NA	NA
SFA 10:0	DNAmTL	NA	NA	NA	NA
SFA 10:0	YangCell	NA	NA	NA	NA
SFA 10:0	DunedinPoAm	NA	NA	NA	NA
SFA 12:0	Horvath1	NA	NA	NA	NA
SFA 12:0	Horvath2	NA	NA	NA	NA
SFA 12:0	Hannum	NA	NA	NA	NA
SFA 12:0	Lin	NA	NA	NA	NA
SFA 12:0	Zhang	NA	NA	NA	NA
SFA 12:0	VidalBralo	NA	NA	NA	NA
SFA 12:0	PhenoAge	NA	NA	NA	NA
SFA 12:0	GrimAge2	NA	NA	NA	NA
SFA 12:0	DNAmTL	NA	NA	NA	NA
SFA 12:0	YangCell	NA	NA	NA	NA
SFA 12:0	DunedinPoAm	NA	NA	NA	NA
SFA 14:0	Horvath1	NA	NA	NA	NA
SFA 14:0	Horvath2	NA	NA	NA	NA
SFA 14:0	Hannum	NA	NA	NA	NA
SFA 14:0	Lin	NA	NA	NA	NA
SFA 14:0	Zhang	NA	NA	NA	NA
SFA 14:0	VidalBralo	NA	NA	NA	NA
SFA 14:0	PhenoAge	NA	NA	NA	NA
SFA 14:0	GrimAge2	NA	NA	NA	NA
SFA 14:0	DNAmTL	NA	NA	NA	NA
SFA 14:0	YangCell	NA	NA	NA	NA
SFA 14:0	DunedinPoAm	NA	NA	NA	NA
SFA 16:0	Horvath1	NA	NA	NA	NA
SFA 16:0	Horvath2	NA	NA	NA	NA
SFA 16:0	Hannum	NA	NA	NA	NA
SFA 16:0	Lin	NA	NA	NA	NA
SFA 16:0	Zhang	NA	NA	NA	NA
SFA 16:0	VidalBralo	NA	NA	NA	NA
SFA 16:0	PhenoAge	NA	NA	NA	NA
SFA 16:0	GrimAge2	NA	NA	NA	NA
SFA 16:0	DNAmTL	NA	NA	NA	NA
SFA 16:0	YangCell	NA	NA	NA	NA
SFA 16:0	DunedinPoAm	NA	NA	NA	NA
SFA 18:0	Horvath1	NA	NA	NA	NA
SFA 18:0	Horvath2	NA	NA	NA	NA
SFA 18:0	Hannum	NA	NA	NA	NA
SFA 18:0	Lin	NA	NA	NA	NA
SFA 18:0	Zhang	NA	NA	NA	NA
SFA 18:0	VidalBralo	NA	NA	NA	NA
SFA 18:0	PhenoAge	NA	NA	NA	NA
SFA 18:0	GrimAge2	NA	NA	NA	NA
SFA 18:0	DNAmTL	NA	NA	NA	NA
SFA 18:0	YangCell	NA	NA	NA	NA
SFA 18:0	DunedinPoAm	NA	NA	NA	NA
MUFA 16:1	Horvath1	NA	NA	NA	NA
MUFA 16:1	Horvath2	NA	NA	NA	NA
MUFA 16:1	Hannum	NA	NA	NA	NA
MUFA 16:1	Lin	NA	NA	NA	NA
MUFA 16:1	Zhang	NA	NA	NA	NA
MUFA 16:1	VidalBralo	NA	NA	NA	NA
MUFA 16:1	PhenoAge	NA	NA	NA	NA
MUFA 16:1	GrimAge2	NA	NA	NA	NA
MUFA 16:1	DNAmTL	NA	NA	NA	NA
MUFA 16:1	YangCell	NA	NA	NA	NA
MUFA 16:1	DunedinPoAm	NA	NA	NA	NA
MUFA 18:1	Horvath1	NA	NA	NA	NA
MUFA 18:1	Horvath2	NA	NA	NA	NA
MUFA 18:1	Hannum	NA	NA	NA	NA
MUFA 18:1	Lin	NA	NA	NA	NA
MUFA 18:1	Zhang	NA	NA	NA	NA
MUFA 18:1	VidalBralo	NA	NA	NA	NA
MUFA 18:1	PhenoAge	NA	NA	NA	NA
MUFA 18:1	GrimAge2	NA	NA	NA	NA
MUFA 18:1	DNAmTL	NA	NA	NA	NA
MUFA 18:1	YangCell	NA	NA	NA	NA
MUFA 18:1	DunedinPoAm	NA	NA	NA	NA
MUFA 20:1	Horvath1	NA	NA	NA	NA
MUFA 20:1	Horvath2	NA	NA	NA	NA
MUFA 20:1	Hannum	NA	NA	NA	NA
MUFA 20:1	Lin	NA	NA	NA	NA
MUFA 20:1	Zhang	NA	NA	NA	NA
MUFA 20:1	VidalBralo	NA	NA	NA	NA
MUFA 20:1	PhenoAge	NA	NA	NA	NA
MUFA 20:1	GrimAge2	NA	NA	NA	NA
MUFA 20:1	DNAmTL	NA	NA	NA	NA
MUFA 20:1	YangCell	NA	NA	NA	NA
MUFA 20:1	DunedinPoAm	NA	NA	NA	NA
MUFA 22:1	Horvath1	NA	NA	NA	NA
MUFA 22:1	Horvath2	NA	NA	NA	NA
MUFA 22:1	Hannum	NA	NA	NA	NA
MUFA 22:1	Lin	NA	NA	NA	NA
MUFA 22:1	Zhang	NA	NA	NA	NA
MUFA 22:1	VidalBralo	NA	NA	NA	NA
MUFA 22:1	PhenoAge	NA	NA	NA	NA
MUFA 22:1	GrimAge2	NA	NA	NA	NA
MUFA 22:1	DNAmTL	NA	NA	NA	NA
MUFA 22:1	YangCell	NA	NA	NA	NA
MUFA 22:1	DunedinPoAm	NA	NA	NA	NA
PUFA 18:2	Horvath1	NA	NA	NA	NA
PUFA 18:2	Horvath2	NA	NA	NA	NA
PUFA 18:2	Hannum	NA	NA	NA	NA
PUFA 18:2	Lin	NA	NA	NA	NA
PUFA 18:2	Zhang	NA	NA	NA	NA
PUFA 18:2	VidalBralo	NA	NA	NA	NA
PUFA 18:2	PhenoAge	NA	NA	NA	NA
PUFA 18:2	GrimAge2	NA	NA	NA	NA
PUFA 18:2	DNAmTL	NA	NA	NA	NA
PUFA 18:2	YangCell	NA	NA	NA	NA
PUFA 18:2	DunedinPoAm	NA	NA	NA	NA
PUFA 20:4	Horvath1	NA	NA	NA	NA
PUFA 20:4	Horvath2	NA	NA	NA	NA
PUFA 20:4	Hannum	NA	NA	NA	NA
PUFA 20:4	Lin	NA	NA	NA	NA
PUFA 20:4	Zhang	NA	NA	NA	NA
PUFA 20:4	VidalBralo	NA	NA	NA	NA
PUFA 20:4	PhenoAge	NA	NA	NA	NA
PUFA 20:4	GrimAge2	NA	NA	NA	NA
PUFA 20:4	DNAmTL	NA	NA	NA	NA
PUFA 20:4	YangCell	NA	NA	NA	NA
PUFA 20:4	DunedinPoAm	NA	NA	NA	NA
PUFA 18:3	Horvath1	NA	NA	NA	NA
PUFA 18:3	Horvath2	NA	NA	NA	NA
PUFA 18:3	Hannum	NA	NA	NA	NA
PUFA 18:3	Lin	NA	NA	NA	NA
PUFA 18:3	Zhang	NA	NA	NA	NA
PUFA 18:3	VidalBralo	NA	NA	NA	NA
PUFA 18:3	PhenoAge	NA	NA	NA	NA
PUFA 18:3	GrimAge2	NA	NA	NA	NA
PUFA 18:3	DNAmTL	NA	NA	NA	NA
PUFA 18:3	YangCell	NA	NA	NA	NA
PUFA 18:3	DunedinPoAm	NA	NA	NA	NA
PUFA 20:5	Horvath1	NA	NA	NA	NA
PUFA 20:5	Horvath2	NA	NA	NA	NA
PUFA 20:5	Hannum	NA	NA	NA	NA
PUFA 20:5	Lin	NA	NA	NA	NA
PUFA 20:5	Zhang	NA	NA	NA	NA
PUFA 20:5	VidalBralo	NA	NA	NA	NA
PUFA 20:5	PhenoAge	NA	NA	NA	NA
PUFA 20:5	GrimAge2	NA	NA	NA	NA
PUFA 20:5	DNAmTL	NA	NA	NA	NA
PUFA 20:5	YangCell	NA	NA	NA	NA
PUFA 20:5	DunedinPoAm	NA	NA	NA	NA
PUFA 22:5	Horvath1	NA	NA	NA	NA
PUFA 22:5	Horvath2	NA	NA	NA	NA
PUFA 22:5	Hannum	NA	NA	NA	NA
PUFA 22:5	Lin	NA	NA	NA	NA
PUFA 22:5	Zhang	NA	NA	NA	NA
PUFA 22:5	VidalBralo	NA	NA	NA	NA
PUFA 22:5	PhenoAge	NA	NA	NA	NA
PUFA 22:5	GrimAge2	NA	NA	NA	NA
PUFA 22:5	DNAmTL	NA	NA	NA	NA
PUFA 22:5	YangCell	NA	NA	NA	NA
PUFA 22:5	DunedinPoAm	NA	NA	NA	NA
PUFA 22:6	Horvath1	NA	NA	NA	NA
PUFA 22:6	Horvath2	NA	NA	NA	NA
PUFA 22:6	Hannum	NA	NA	NA	NA
PUFA 22:6	Lin	NA	NA	NA	NA
PUFA 22:6	Zhang	NA	NA	NA	NA
PUFA 22:6	VidalBralo	NA	NA	NA	NA
PUFA 22:6	PhenoAge	NA	NA	NA	NA
PUFA 22:6	GrimAge2	NA	NA	NA	NA
PUFA 22:6	DNAmTL	NA	NA	NA	NA
PUFA 22:6	YangCell	NA	NA	NA	NA
PUFA 22:6	DunedinPoAm	NA	NA	NA	NA