Not within spitting distance: salivary immunoassays of estradiol have subpar validity for cycle phase

Ruben Arslan

What properties do different measures of estradiol and progesterone have?

Below, you’ll find a complete tabular summary of the methods and results for each of the datasets (columns).

Tabular summary of analyses

Show code 
library(tidyverse)
library(gt)
library(brms)
source("0_summary_functions.R")
summary_table <- function(Hormone) {
  summaries <- list.files("summaries", full.names = T)
  summaries <- summaries[!str_detect(summaries, "(Ovulatory|OCMATE HC|BioCycle_Estradiol|Estradiol LCMS|\\.02\\.)")]
  summaries <- summaries[str_detect(summaries, pattern = Hormone)]
  summary_names <- str_match(summaries, "table_(.+?)\\.rds")[,2]
  message(paste(summary_names, "\n"))
  sus <- list()
  su_dfs <- list()
  for(s in seq_along(summaries)) {
    su <- rio::import(summaries[s])
    su_df <- su
    su_df$Sample <- summary_names[s]
    su_df$imputed_fc_vs_measured_graph <- 
    su_df$imputed_bc_vs_measured_graph <- 
    su_df$imputed_lh_vs_measured_graph <- 
    su_df$bc_day_model <- 
    su_df$fc_day_model <- 
    su_df$lh_day_model <- 
    su_df$distribution <- NULL
    su_df$imputed_fc_vs_measured_graph <- 
    su_df$imputed_bc_vs_measured_graph <- 
    su_df$imputed_lh_vs_measured_graph <- 
    su_df$Distribution <- 
    su_df$plot_bc <- 
    su_df$plot_fc <- 
    su_df$plot_lh <- summary_names[s]
    sus[[summary_names[s]]] <- su
    su_dfs[[summary_names[s]]] <- su_df %>% as_tibble() %>% mutate_if(is.numeric, ~ sprintf("%.2f", .))
  }

  comp_wide <- as_tibble(bind_rows(su_dfs, .id = "Sample")) %>%
    mutate_at(vars(n_women, n_cycles, n_days, missing, censored, outliers, usable_n, usable_n_women), 
              ~ str_sub(., 1, -4))
  
  
  comp_wide <- comp_wide %>%
    mutate_at(vars(missing, censored, outliers), ~ sprintf("%.0f (%.0f%%)", as.numeric(.), 100*as.numeric(.)/as.numeric(n_days)))

  comp_wide <- comp_wide %>% 
    mutate(Measure = paste0("LOD=", `Limit of detection`, ", LOQ=", LOQ, ", Intraassay CV=",`Intraassay CV`, ", Interassay CV=", `Interassay CV`)) %>% 
    select(-`Limit of detection`, -`Intraassay CV`,
           -`Interassay CV`, -LOQ)

  Hormone_label <- names(sort(table(comp_wide$Hormone),decreasing = T))[1]

  comp_wide <- comp_wide %>% 
    select(Sample, Dataset, Hormone, Citation, Method,
           Measure, Procedure, Scheduling, LH_test,
           Age = age,
           `In Relationship` = in_relationship,
           `Cycle length` = cycle_length,
           Women = n_women,
           Cycles = n_cycles,
           Days = n_days,
           `% women no LH surge` = no_lh_surge_woman,
           `% cycles no LH surge` = no_lh_surge_cycle,
           `Usable n women` = usable_n_women,
           `Usable n days` = usable_n,
           Distribution,
           logmean, logsd, missing, outliers, censored,
           mean, sd, median, mad, range,
           var_id_loo, var_cycle_loo,
           var_id, var_cycle, var_resid,
           r_fc_stirn, r_log_fc_stirn, r_diff_fc_stirn, r_log_diff_fc_stirn, r_loo_fc, r_fc_imputed, 
           r_diff_fc_imputed, sd_fc_imputed, r_diff_fc_imputed_rr, 
           plot_fc,
           r_bc_stirn, r_log_bc_stirn, r_diff_bc_stirn, r_log_diff_bc_stirn, r_loo_bc, r_bc_imputed, 
           r_diff_bc_imputed, sd_bc_imputed, r_diff_bc_imputed_rr, 
           plot_bc,
          r_prob_lh, r_log_prob_lh, r_diff_prob_lh, r_log_diff_prob_lh, r_loo_lh, r_lh_imputed, 
           r_diff_lh_imputed, sd_lh_imputed, r_diff_lh_imputed_rr, 
           plot_lh,
           everything(), -matches("rmse_[^i]"), -contains("_vs_measured"))
  
  study_names <- c("BioCycle", "Roney 2013", "OCMATE Non-HC", "Marcinkowska 2020", "GOL2", "GOCD2", "Blake 2017", "Grebe et al. 2016")
  short_names <- c("BioCycle", "Roney '13", "OCMATE", "Marcinkowska '20", "Stern '21", "Jünger '18",  "Blake '17", "Grebe '16")
  names(short_names) <- study_names
  comp_wide$Dataset <- factor(as.character(short_names[comp_wide$Dataset]), levels = short_names)

  if(n_distinct(comp_wide$Sample) == n_distinct(comp_wide$Dataset)) {
    comp_wide$Sample <- comp_wide$Dataset
  }

  comp <- comp_wide %>% 
    pivot_longer(-Sample)
  
  # comp$Sample <- str_trim(str_replace(str_replace(str_replace(
  #   comp$Sample, Hormone, ""), "Free", ""), "_", " "))

  comp <- comp %>% arrange(Sample)

  comp <- comp %>% 
    pivot_wider(name, Sample) %>% 
    rename(rowname = name)

  comp <- comp %>% 
    mutate(rowgroup = case_when(
      rowname %in% c("Hormone", "Dataset","Citation", "Scheduling") ~ "Study",
      rowname %in% c("Method", "Measure", "Procedure", "LH_test") ~ "Measure",
      rowname %in% c("Women", "Cycles", "Days", "Usable n women", "Usable n days", "Age", "In Relationship", "Cycle length", "% women no LH surge", "% cycles no LH surge") ~ "Sample",
      rowname %in% c("Distribution", 
                     "logmean", "logsd", "missing", "censored", "outliers",
                     "mean", "sd", "median", "mad", "range") ~ "Descriptive statistics",
      rowname %in%  c("var_id_loo", "var_cycle_loo", "var_id", "var_cycle", "var_resid") ~ "Interindividual differences",
    rowname %in%  c("r_fc", "r_loo_fc", "plot_fc", "imputed_fc_vs_measured_graph",
           "r_fc_stirn", "r_log_fc_stirn", "r_diff_fc_stirn", "r_log_diff_fc_stirn", "rmse_fc", "r_fc_imputed", "r_diff_fc_imputed", "sd_fc_imputed", "r_diff_fc_imputed_rr",  "rmse_fc_imputed") ~ "Validity (Forward counting)",
    rowname %in%  c("r_bc", "r_loo_bc", "plot_bc", "imputed_bc_vs_measured_graph",
           "r_bc_stirn", "r_log_bc_stirn", "r_diff_bc_stirn", "r_log_diff_bc_stirn", "rmse_bc", "r_bc_imputed", "r_diff_bc_imputed", "sd_bc_imputed", "r_diff_bc_imputed_rr", "rmse_bc_imputed") ~ "Validity (Backward counting)",
    rowname %in%  c("r_lh", "r_loo_lh", "plot_lh", "imputed_lh_vs_measured_graph",
           "r_prob_lh", "r_log_prob_lh", "r_diff_prob_lh", "r_log_diff_prob_lh", "rmse_lh", "r_lh_imputed", "r_diff_lh_imputed", "sd_lh_imputed", "r_diff_lh_imputed_rr",  "rmse_lh_imputed") ~ "Validity (LH)"
    ))
  
  comp %>% 
  gt(groupname_col = "rowgroup") %>% 
  tab_header(
    title = paste0(Hormone_label, " measures"),
    subtitle = "Comparing salivary and serum measures on a variety of performance criteria"
  ) %>% 
  text_transform(
    locations = cells_body(everything(), rowname == "Citation"),
      fn = function(x) {
            long <- x
            short <- str_match(str_trim(x), "^(.+?\\))")[,2]
            str_c("<small><details><summary>", short,"</summary>", long, "</details></small>")
        }
  ) %>% 
  text_transform(
    locations = cells_body(everything(), rowname %in% c("Procedure", "Scheduling", "LH_test")),
      fn = function(x) {
          long <- x
          short <- str_match(x, "^(.+?)'")[,2]
          long <- str_match(x, "'(.+)'")[,2]
          short[is.na(short)] <- ""
          long[is.na(long)] <- x[is.na(long)]
          details <- str_c("<small>", short, "<details><summary>More</summary>", long, "</details></small>")
          details[is.na(x) | x == ""] <- ""
          details
        }
  ) %>% 
  text_transform(
    locations = cells_body(everything(), rowname == "Distribution"),
      fn = function(x) {
        plots <- character(length(x))
        for(i in seq_along(x)) {
          message(x[i])
          if(!is.null(sus[[x[i]]]$distribution)) {
            if(Hormone_label == "Estradiol") {
              breaks <- c(0.2, 1, 2, 10, 15, 20, 30)
              limits <- c(0.2, 32)
            } else {
              breaks <- c(0.2, 1, 5, 10, 50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 30000)
              limits <- c(0.2, 30000)
            }
            plots[i] <- { sus[[x[i]]]$distribution +
            scale_x_continuous(Hormone_label, breaks = breaks, labels = breaks, 
                               limits = limits, trans = "log") +
            scale_fill_manual(values = c("none" = "black", "left" = "gray"), guide = FALSE) +
            geom_vline(xintercept = as.numeric(sus[[x[i]]]$`Limit of detection`),
                       linetype = 'solid') +
            geom_vline(xintercept = as.numeric(sus[[x[i]]]$LOQ),
                       linetype = 'dashed') +
            theme_minimal(base_size = 8) } %>%
            ggplot_image(height = px(200), aspect_ratio = 0.7)
          }
        }
        plots
      }
    ) %>% text_transform(
    locations = cells_body(everything(), rowname == "plot_fc"),
      fn = function(x) {
        plots <- character(length(x))
        for(i in seq_along(x)) {
          message(x[i])
          if(!is.null(sus[[x[i]]]$fc_day_model)) {
          plots[i] <- cycle_phase_plot(sus[[x[i]]], "fc_day_model", size = 6) %>%
            ggplot_image(height = px(600), aspect_ratio = 0.33)
          } else {
            plots[i] <- "NA"
          }
        }
        plots
      }
    ) %>% text_transform(
    locations = cells_body(everything(), rowname == "plot_bc"),
      fn = function(x) {
        plots <- character(length(x))
        for(i in seq_along(x)) {
          message(x[i])
          if(!is.null(sus[[x[i]]]$bc_day_model)) {
          plots[i] <- cycle_phase_plot(sus[[x[i]]], "bc_day_model", size = 6) %>%
            ggplot_image(height = px(600), aspect_ratio = 0.33)
          } else {
            plots[i] <- "NA"
          }
        }
        plots
      }
    ) %>% text_transform(
    locations = cells_body(everything(), rowname == "plot_lh"),
      fn = function(x) {
        plots <- character(length(x))
        for(i in seq_along(x)) {
          message(x[i])
          if(!is.null(sus[[x[i]]]$lh_day_model)) {
          plots[i] <- cycle_phase_plot(sus[[x[i]]], "lh_day_model", size = 6) %>%
            ggplot_image(height = px(600), aspect_ratio = 0.33)
          } else {
            plots[i] <- "NA"
          }
        }
        plots
      }
    ) %>%  
    tab_style(
    style = list(
      cell_text(weight = "bold",
                size = "large")
      ),
    locations = cells_row_groups()
  ) %>%
    cols_width(everything() ~ px(230)
  )
}

ratio_summary_table <- function() {
  summaries <- list.files("summaries", full.names = T)
  summaries <- summaries[str_detect(summaries, pattern = "ratio")]
  summaries <- summaries[!str_detect(summaries, "(Ovulatory|OCMATE HC|Dinh)")]
  summary_names <- str_match(summaries, "table_(.+?)\\.")[,2]
  sus <- list()
  su_dfs <- list()
  for(s in seq_along(summaries)) {
    su <- rio::import(summaries[s])
    su_df <- su
    su_df$m_bc_prob_rat <- 
      su_df$m_fc_prob_rat <- 
      su_df$m_lh_prob_rat <- 
      su_df$m_bc_prob <- 
      su_df$m_fc_prob <- 
      su_df$m_lh_prob <- 
      su_df$scatterplot <- NULL
    su_df$plot_scatter <- summary_names[s]
    sus[[summary_names[s]]] <- su
    su_dfs[[summary_names[s]]] <- su_df
  }

comp_wide <- as_tibble(bind_rows(su_dfs, .id = "Sample")) %>%
  mutate_if(is.numeric, ~ sprintf("%.2f", .)) %>% 
  mutate_at(vars(n_women, n_cycles, n_days, usable_n), ~ str_sub(., 1, -4)) %>% 
  select(Dataset, Method,
         Women = n_women,
         Cycles = n_cycles,
         Days = n_days,
         `Usable n` = usable_n,
         plot_scatter,
         r_ep, r_log_ep, r_e_ratio, r_log_e_ratio, r_p_ratio, r_log_p_ratio, r_bc_ratio, r_bc_log_ratio, r_erat_bc, r_loo_bc, r_fc_ratio, r_fc_log_ratio, r_erat_fc, r_loo_fc, r_lh_ratio, r_lh_log_ratio, r_erat_lh, r_loo_lh,
         everything())


  study_names <- c("BioCycle", "Roney 2013", "OCMATE Non-HC", "Marcinkowska 2020", "GOL2", "GOCD2", "Blake 2017", "Grebe et al. 2016")
  short_names <- c("BioCycle", "Roney '13", "OCMATE", "Marcinkowska '20", "Jünger '18", "Stern '21", "Blake '17", "Grebe '16")
  names(short_names) <- study_names
  comp_wide$Dataset <- factor(as.character(short_names[comp_wide$Dataset]), levels = short_names)

  if(n_distinct(comp_wide$Sample) == n_distinct(comp_wide$Dataset)) {
    comp_wide$Sample <- comp_wide$Dataset
  }

comp <- comp_wide %>% 
  pivot_longer(-Sample) %>% 
  pivot_wider(name, Sample) %>% 
  rename(rowname = name)

colnames(comp) <- str_trim(str_replace(
  colnames(comp), "_", " "))

Hormone_label <- names(sort(table(comp_wide$Hormone),decreasing = T))[1]

comp <- comp %>% 
  mutate(rowgroup = case_when(
    rowname %in% c("Dataset", "Method", "Hormone") ~ "Measure",
    rowname %in% c("Women", "Cycles", "Days", "Usable n") ~ "Sample size",
  TRUE ~ "Relationship"
  )) 

comp %>% 
gt(groupname_col = "rowgroup") %>% 
  tab_header(
    title = paste0("Estradiol and progesterone measures"),
    subtitle = "Comparing salivary and serum measures on a variety of performance criteria"
  ) %>% 
  text_transform(
    locations = cells_body(everything(), 7),
      fn = function(x) {
        plots <- character(length(x))
        for(i in seq_along(x)) {
          message(x[i])
          if(!is.null(sus[[x[i]]]$scatterplot)) {
            plots[i] <- { sus[[x[i]]]$scatterplot +
            theme_minimal() } %>%
            ggplot_image(height = px(200))
          }
        }
        plots
      }
    )
}

Estradiol (E2)

Show code 
summary_table("Estradiol")
Estradiol measures
Comparing salivary and serum measures on a variety of performance criteria
BioCycle Roney '13 OCMATE Marcinkowska '20 Stern '21 Jünger '18 Blake '17 Grebe '16
Study
Dataset BioCycle Roney '13 OCMATE Marcinkowska '20 Stern '21 Jünger '18 Blake '17 Grebe '16
Hormone Free Estradiol Estradiol Estradiol Estradiol Estradiol Estradiol Estradiol Estradiol
Citation
Wactawski-Wende, J., Schisterman, E. F., Hovey, K. M., Howards, P. P., Browne, R. W., Hediger, M., Liu, A., Trevisan, M., & BioCycle Study Group. (2009) Wactawski-Wende, J., Schisterman, E. F., Hovey, K. M., Howards, P. P., Browne, R. W., Hediger, M., Liu, A., Trevisan, M., & BioCycle Study Group. (2009). BioCycle study: design of the longitudinal study of the oxidative stress and hormone variation during the menstrual cycle. Paediatric and Perinatal Epidemiology, 23(2), 171–184. https://doi.org/10.1111/j.1365-3016.2008.00985.x Howards, P. P., Schisterman, E. F., Wactawski-Wende, J., Reschke, J. E., Frazer, A. A., & Hovey, K. M. (2009). Timing clinic visits to phases of the menstrual cycle by using a fertility monitor: the BioCycle Study. American Journal of Epidemiology, 169(1), 105–112. https://doi.org/10.1093/aje/kwn287 Prasad, A., Mumford, S. L., Buck Louis, G. M., Ahrens, K. A., Sjaarda, L. A., Schliep, K. C., Perkins, N. J., Kissell, K. A., Wactawski-Wende, J., & Schisterman, E. F. (2014). Sexual activity, endogenous reproductive hormones and ovulation in premenopausal women. Hormones and Behavior, 66(2), 330–338. https://doi.org/10.1016/j.yhbeh.2014.06.012
Roney, J. R., & Simmons, Z. L. (2013)Roney, J. R., & Simmons, Z. L. (2013). Hormonal predictors of sexual motivation in natural menstrual cycles. Hormones and Behavior, 63(4), 636–645. https://doi.org/10.1016/j.yhbeh.2013.02.013
Jones, B. C., Hahn, A. C., Fisher, C. I., Wang, H., Kandrik, M., Han, C., Fasolt, V., Morrison, D., Lee, A. J., Holzleitner, I. J., O’Shea, K. J., Roberts, S. C., Little, A. C., & DeBruine, L. M. (2018)Jones, B. C., Hahn, A. C., Fisher, C. I., Wang, H., Kandrik, M., Han, C., Fasolt, V., Morrison, D., Lee, A. J., Holzleitner, I. J., O’Shea, K. J., Roberts, S. C., Little, A. C., & DeBruine, L. M. (2018). No Compelling Evidence that Preferences for Facial Masculinity Track Changes in Women’s Hormonal Status. Psychological Science, 29(6), 996–1005. https://doi.org/10.1177/0956797618760197
Marcinkowska, U. M. (2020)Marcinkowska, U. M. (2020). Importance of Daily Sex Hormone Measurements Within the Menstrual Cycle for Fertility Estimates in Cyclical Shifts Studies. Evolutionary Psychology: An International Journal of Evolutionary Approaches to Psychology and Behavior, 18(1), 1474704919897913. https://doi.org/10.1177/1474704919897913 Marcinkowska, U. M., Ellison, P. T., Galbarczyk, A., Milkowska, K., Pawlowski, B., Thune, I., & Jasienska, G. (2016). Lack of support for relation between woman’s masculinity preference, estradiol level and mating context. Hormones and Behavior, 78, 1–7. https://doi.org/10.1016/j.yhbeh.2015.10.012 Marcinkowska, U. M., Galbarczyk, A., & Jasienska, G. (2018). La donna è mobile? Lack of cyclical shifts in facial symmetry, and facial and body masculinity preferences-A hormone based study. Psychoneuroendocrinology, 88, 47–53. https://doi.org/10.1016/j.psyneuen.2017.11.007 Marcinkowska, U. M., Kaminski, G., Little, A. C., & Jasienska, G. (2018). Average ovarian hormone levels, rather than daily values and their fluctuations, are related to facial preferences among women. Hormones and Behavior, 102, 114–119. https://doi.org/10.1016/j.yhbeh.2018.05.013
Stern, J., Kordsmeyer, T. L., & Penke, L. (2021)Stern, J., Kordsmeyer, T. L., & Penke, L. (2021). A longitudinal evaluation of ovulatory cycle shifts in women’s mate attraction and preferences. Hormones and Behavior, 128, 104916. https://doi.org/10.1016/j.yhbeh.2020.104916
Jünger, J., Kordsmeyer, T. L., Gerlach, T. M., & Penke, L. (2018)Jünger, J., Kordsmeyer, T. L., Gerlach, T. M., & Penke, L. (2018). Fertile women evaluate male bodies as more attractive, regardless of masculinity. Evolution and Human Behavior: Official Journal of the Human Behavior and Evolution Society, 39(4), 412–423. https://doi.org/10.1016/j.evolhumbehav.2018.03.007
Blake, K. R., Bastian, B., O’Dean, S. M., & Denson, T. F. (2017)Blake, K. R., Bastian, B., O’Dean, S. M., & Denson, T. F. (2017). High estradiol and low progesterone are associated with high assertiveness in women. Psychoneuroendocrinology, 75, 91–99. http://www.sciencedirect.com/science/article/pii/S0306453016302190
Grebe, N. M., Emery Thompson, M., & Gangestad, S. W. (2016)Grebe, N. M., Emery Thompson, M., & Gangestad, S. W. (2016). Hormonal predictors of women’s extra-pair vs. in-pair sexual attraction in natural cycles: Implications for extended sexuality. Hormones and Behavior, 78, 211–219. https://doi.org/10.1016/j.yhbeh.2015.11.008
Scheduling Distributed across cycle
More, regularly menstruating premenopausal women for two menstrual cycles. Participants visited the clinic up to 8 times per cycle, at which time blood and urine were collected. The visits occurred at key hormonally defined phases of the menstrual cycle, with the help of an algorithm based on cycle length and data from a fertility monitor. The study included 16 cycle visits (eight per cycle) over two cycles. The timing of these visits was scheduled to correspond with specific times of the menstrual cycle where the most hormonal variation would be expected, approximately days 2, 7, 12, 13, 14, 18, 22 and 27 of a 28-day cycle, adjusted for cycle length (Table 4). Cycle visits were routinely scheduled between 7:00 AM and 8:30 AM to allow for collection of fasting samples and to reduce diurnal variation.
 Every day for up to two cycles.
MoreHere we collected daily saliva samples across 1–2 menstrual cycles from a sample of naturally cycling young women, as well as daily diary reports of both sexual behavior and self-reported sexual desire.
 Irrespective of cycle.
MoreParticipants completed up to three blocks of test sessions (mean time between Block 1 and Block 2 = 230 days; mean time between Block 2 and Block 3 = 487 days). Each of the three blocks of test sessions consisted of five weekly test sessions. Table 1 shows how many women completed one, two, three, four, or five test sessions in Blocks 1 through 3.
 Whole cycle
MoreParticipants were asked to collect saliva samples each morning starting from the first day of menstrual bleeding, until the end of the menstrual cycle (i.e., the day before the onset of the next menstrual bleeding). Due to financial restrictions on average 15 hormonal measurements were done per participant. For E centred around ovulation, for P last 15 days of the cycle.
 peri-ovulatory (2x), mid-luteal und premenstrual
MoreSessions two to five were computer-based testing sessions and took place across different phases of the ovulatory cycle, scheduled based on backward counting and the observed LH test surge. Suitable testing days were computed with the help of an Excel sheet created for that purpose (see open material). All participants completed two sessions in their expected fertile phase (mid to late follicular phase, approx. 5–6 days before ovulation) and two sessions in their expected luteal phase (one session in the mid luteal phase, one session in the premenstrual phase). Scheduling was validated via LH test results and via following up to the day of the next menstrual onset. Details can be found in the supple- mentary material. The starting session for each participant depended on their current cycle phase at the introductory session and their personal schedule. Of all participants who finished all sessions, 134 participants started with the first session in their fertile phase, and 123 started in the luteal phase.
 Peri-ovulatory & Luteal
MoreSessions two to five, the computer-based testing sessions, took place across two ovulatory cycles per participant, once per cycle during the fertile and once during the luteal phase. To control for possible effects of diurnal changes in hormone levels (Bao et al., 2003; Veldhuis et al., 1988), all sessions took place in the second half of the day (mainly between 11.30 am and 6 pm).
 Fertile and non-fertile phase.
MoreThe repeated design of the study required that all participants attended one fertile and one non-fertile laboratory session. Hence, all participants included here detected a LH surge and thus had ovulatory cycles. Forty-seven percent of non-fertile sessions occurred in the early follicular phase (mean cycle day = 4.02, SD = 2.54) and the remain- der occurred in the late luteal phase (mean cycle day=26.06, SD=3.45). Most participants completed both fertile and non- fertile sessions in the same menstrual cycle (63.5%), the remainder attended sessions occurring 1, 2, or 3 cycles apart. Once a positive LH surge was detected, 85.4% of participants attended their fertile session within one day, the remaining 14.6% attended this session within two days. Following a surge in LH, ovulation is expected to occur approximately 24–48 h later (Direito et al., 2013).2
 Irrespective of cycle phase.
MoreWe do emphasize that, although we sought to representatively sample days and phases of the cycle, by chance the luteal phase appears to have been overrepresented in our sample.
Measure
Method Serum CL-EIA (chemiluminescence enzyme immunoassay) + mass-action-algorithm Salivary immunoassay (Salimetrics) Salivary immunoassay (Salimetrics) Salivary immunoassay (DRG) Salivary immunoassay (IBL) Salivary immunoassay (IBL) Salivary immunoassay (IBL) Salivary immunoassay (Salimetrics)
Measure LOD=0.21, LOQ=NA, Intraassay CV=NA, Interassay CV=<10 LOD=0.10, LOQ=1.00, Intraassay CV=4.52, Interassay CV=8.15 LOD=0.10, LOQ=1.00, Intraassay CV=7.13, Interassay CV=7.45 LOD=0.40, LOQ=1.00, Intraassay CV=7.5%, Interassay CV=10.1% LOD=2.10, LOQ=2.10, Intraassay CV=8.80, Interassay CV=11.80 LOD=0.30, LOQ=0.90, Intraassay CV=9.70, Interassay CV=11.30 LOD=2.10, LOQ=NA, Intraassay CV=<12, Interassay CV=<12 LOD=0.10, LOQ=1.00, Intraassay CV=6.58, Interassay CV=2.68
Procedure
MoreBlood collection and handling protocols were designed to minimize variability (Wactawski- Wende et al., 2009). All samples were processed and frozen at −80°C within 90 minutes of phlebotomy and analytes were measured in participant-specific batches within a single run to limit analytical variability. Estradiol, LH, follicle-stimulating hormone (FSH), and progesterone concentrations were measured in serum samples using solid-phase competitive chemiluminescent enzymatic immunoassays (DPC Immulite 2000 analyzer, Siemens Medical Solutions Diagnostics, Deerfield, IL) at the Kaleida Health Center for Laboratory Medicine (Buffalo, NY).
MoreWomen were also instructed to collect a saliva sample each morn- ing. They were asked to do so at least 30 min after any eating or drinking, ideally upon first waking. Collection was via passive drool (a few minutes after rinsing with clean water) into pre-labeled poly- propylene vials. Women stored these vials in home freezers and then delivered them weekly to our research lab, at which time they were given a new batch of vials. Saliva samples were then stored at − 80 C until being shipped for assay. Saliva samples were shipped on dry ice to the Endocrine Core Laboratory at the California Regional Primate Research Center, Davis, CA. Samples were centrifuged at 3000 rpm for 20 min. to separate the aqueous component from other particles. Concentrations of progester- one were estimated in duplicate using commercial radioimmunoassay kits (Siemens Health Diagnostics, Inc., Los Angeles, CA). Estradiol concentrations were estimated in duplicate using the high sensitivity salivary 17β-estradiol enzyme immunoassay kit (Salimetrics LLC, State College, PA). The estradiol assay has a least detectable dose of 0.1 pg/ml; intra- and inter-assay CVs were 4.52% and 8.15%, respectively.
MoreParticipants provided a saliva sample via passive drool (Papacosta & Nassis, 2011) in each test session. Partici- pants were instructed to avoid consuming alcohol and coffee in the 12 hr prior to participation and avoid eating, smoking, drinking, chewing gum, or brushing their teeth in the 60 min prior to participation. Each woman’s test sessions took place at approximately the same time of day to minimize effects of diurnal changes in hormone levels (Bao et al., 2003; Veldhuis et al., 1988). Saliva samples were frozen immediately and stored at −32° C until being shipped on dry ice to the Salimet- rics Lab (Suffolk, United Kingdom) for analysis, where they were assayed using the Salivary 17β-Estradiol Enzyme Immunoassay Kit 1-3702 (M = 3.30 pg/mL, SD = 1.27 pg/mL, sensitivity = 0.1 pg/mL, intra-assay coefficient of variability, or CV = 7.13%, inter-assay CV = 7.45%).
MoreParticipants were asked to collect saliva samples each morning starting from the first day of menstrual bleeding, until the end of the menstrual cycle (i.e., the day before the onset of the next menstrual bleeding). Women were verbally instructed by the principal investigator concerning collecting and storing the saliva and were given a set of 2-ml centrifuge tubes with the minimum amount of required saliva marked on a tube together with written instructions. Saliva samples were collected in the morning, preferably before eating, drinking, or smoking, or not earlier than 30 min after eating, drinking, or smoking. Immediately after collec- tion, samples were frozen in the participant’s home freezer. After the end of the cycle, samples were transported in portable freezers to the laboratory where hormonal assays for measure- ments of 17-b-estradiol (E2) and 17-a-hydroxy-progesterone (P) were conducted. Hormonal measurements were conducted using commercially available hormonal assays of DRG Inter- national, Inc. ELISA plates SLV4188 (sensitivity: 0.4 pg/ml, standard range: 1–100 pg/ml) for E2 and SLV3140 for P (sen- sitivity: 2.5 pg/ml, standard range: 10–5,000 pg/ml). All sam- ples were assayed in duplicates. The quality of hormonal measurements was monitored for each plate separately by including samples of known concentrations with low and high E2 and P levels. Inter-assay variability was 10.1%, intra-assay variability was 7.5% for E2 and 14.1% and 4.9% for P, which is within acceptable values for steroid sex hormones assays (Schultheiss & Stanton, 2009).
MoreFor hormone assays, we collected four saliva samples from each participant (one per testing session). Contamination of saliva samples was minimized by asking participants to abstain from eating, drinking (except plain water), smoking, chewing gum, or brushing teeth for at least one hour before each session. The samples were stored at − 80 ◦C directly after collection until shipment on dry ice to the Kirschbaum Lab at Technical University of Dresden, Germany (one freeze-thaw cycle). The samples were reanalyzed for estradiol using the highly sensitive 17β-estradiol enzyme immunoassay kit (IBL International, Hamburg, Germany). Samples were analyzed in singlets, however, the lab reported that their procedure yields CVs < 11%.
MoreNext, the saliva samples were collected via passive drool. For hormone assays, we collected four saliva samples from each participant (one per testing session). Contamination of saliva samples was minimized by asking participants to abstain from eating, drinking (except plain water), smoking, chewing gum or brushing teeth for at least one hour before each session. The samples were stored at -80°C directly after collection until shipment on dry ice to the Kirschbaum Lab at Technical University of Dresden, Germany, where estradiol, progesterone, testosterone and cortisol was assessed via liquid chromatography mass spectrometry (LCMS; Gao, Stalder, & Kirschbaum, 2015). Since the LCMS analysis of the estradiol levels did only detect 22% of all possible values, the samples were reanalyzed using the highly sensitive 17β-estradiol enzyme immunoassay kit (IBL International, Hamburg, Germany). These latter estradiol values were used in subsequent analyses.
MoreProgesterone and estradiol samples were stored at −20◦C and analyzed by a professional reference laboratory in Dresden, Germany. After thawing, samples were centrifuged at 3000 rpm for five minutes, which resulted in a clear supernatant of low viscosity. Salivary progesterone and estradiol concentrations were measured using commercially available chemiluminescence-immuno-assays with high sensitivity (IBL International, Hamburg, Germany). Intra- and inter-assay coefficients of variations for both hormones were below 12%.
MoreWe asked participants to provide saliva samples (~5 mL) via passive drool, from which we assayed estradiol, progesterone, and testosterone twice during the study: once during an initial session, and once upon awakening the day of a follow-up session one week later. Saliva samples were pipetted from a cup to test tubes, then frozen at -20° C. Prior to assays, frozen samples were thawed, mixed by vortexing, then centrifuged for 15 minutes to break up and precipitate mucins. Salivary 17β estradiol, progesterone, and testosterone concentrations were determined using enzyme-linked immunosorbent assays (ELISA) designed for saliva by Salimetrics LLC (Carlsbad, CA).
LH_test
MoreParticipants were also provided with and trained to use the Clearblue Easy Fertility Monitor (Inverness Medical Innovations, Inc., Waltham, Massachusetts) (www. clearblueeasy.com). The fertility monitor was originally developed to assist women in becoming pregnant by helping them to identify their fertile window through measurement of both estrone- 3-glucuronide (E3G) and LH in urine as described in greater detail elsewhere (13). Briefly, the monitor is synchronized to a woman’s cycle and then is checked daily to see whether a test is requested. Between the sixth and the ninth days of a woman’s cycle (depending on her cycle length history), the monitor begins to request daily tests for 10 days. On test days, the woman briefly submerges a test stick in her first- morning urine and then inserts the test stick into the monitor. The test stick has a nitrocellulose strip with an anti-LH antibody zone and an E3G conjugate zone. The monitor optically reads the level of E3G and LH in the urine by the intensity of the lines in the corresponding zones (13). Each day, the monitor assigns the woman to low, high, or peak fertility on the basis of her E3G and LH levels. Thus, unlike home LH test sticks, the monitor provides informa- tion to help anticipate the LH surge.
MoreAll participants received LH kits. Each kit consisted of sterilized urine cups and 10 LH tests. Women were instructed to conduct the tests from the 10th until the 20th day of the cycle or until a positive result was obtained.
Morehighly sensitive (10mIU) LH urine ovulation test strips from MedNet GmbH. Participants started LH-testing after menstruation (around reverse cycle day 21) and continued until a rise of LH (positive tests) was observed and a minimum of two days after the tests were negative again (as suggested by Roney, 2018b). Participants were provided with a minimum of ten LH tests each and provided daily pictures of the tests to the investigators for confir- mation. LH results were used to allow flexible scheduling in case LH test results differed from the scheduling based on counting (see supplementary material for details).
Morehighly sensitive (10 mIU/ml) urine ovulation test strips from purbay®, which measure the luteinizing hormone (LH). These LH-tests had to be done at home at the estimated day of ovulation and the four days prior to that and results were self- reported by the participants.
MoreTo maximize the likelihood of detecting surges in LH, we fol- lowed the standardized LH testing procedure in Blake et al. (2016b). All participants used commercially available urinary LH tests (Blue Cross Bio-Medical Co. LTD, CE/FDA Registered) and tested until a positive surge was detected. Wave 1 participants tested until a positive result was reported (a maximum of 10days) and Wave 2 tested for 11 continuous days, even if a surge was reported ear- lier. We instructed participants to test between 1000 h and 2000 h and report the result to the research team via SMS or email. The repeated design of the study required that all participants attended one fertile and one non-fertile laboratory session. Hence, all participants included here detected a LH surge and thus had ovulatory cycles. Participants who did not record a surge in LH were not eligible for inclusion in the study (see Blake et al., 2016b for full details of the sampling procedure).
Sample
Age 27.3±8.21 NA 21.5±3.29 NA 23.1±3.28 23.2±3.45 22.7±4.87 NA
In Relationship 25% NA 36% NA 47% 48% 53% NA
Cycle length 28.8±4.10 27.6±5.06 29.7±6.73 28.2±2.99 30.0±4.75 29.5±6.54 29.2±2.50 28.8±3.71
Women 259 43 384 102 257 157 60 33
Cycles 509 122 907 102 454 398 109 33
Days 4078 2367 2394 2265 1028 628 120 66
% women no LH surge 43/257 (17%) NA NA 25/100 (25%) 47/243 (19%) 9/125 (7%) 0/58 (0%) NA
% cycles no LH surge 141/481 (29%) NA NA 25/100 (25%) 145/406 (36%) 57/243 (23%) 0/103 (0%) NA
Usable n women 257 42 360 100 243 157 58 31
Usable n days 3682 1091 1664 1647 914 549 114 58
Descriptive statistics
Distribution
logmean 0.40 1.04 1.13 1.70 1.29 1.57 1.84 0.82
logsd 0.84 0.42 0.41 0.86 0.44 0.67 0.57 0.42
missing 177 (4%) 1169 (49%) 103 (4%) 585 (26%) 1 (0%) 32 (5%) 6 (5%) 4 (6%)
outliers 0 (0%) 0 (0%) 9 (0%) 0 (0%) 4 (0%) 0 (0%) 0 (0%) 0 (0%)
censored 89 (2%) 0 (0%) 0 (0%) 14 (1%) 124 (12%) 3 (0%) 2 (2%) 0 (0%)
mean 2.13 3.10 3.36 7.61 4.00 5.88 7.42 2.45
sd 1.95 1.34 1.55 6.40 1.89 3.93 4.74 0.92
median 1.42 2.89 3.11 5.96 3.71 5.00 6.88 2.25
mad 1.19 1.25 1.23 4.38 1.54 3.13 4.13 0.75
range 0.21, 18.28 0.67, 9.17 0.48, 24.22 0.40, 46.52 1.01, 19.05 0.30, 31.00 2.10, 28.81 0.53, 5.62
Interindividual differences
var_id_loo 0.06 [0.04;0.07] 0.23 [0.18;0.27] 0.47 [0.43;0.51] 0.52 [0.48;0.56] 0.16 [0.10;0.22] 0.14 [0.08;0.19] -0.03 [-0.06;-0.01] 0.23 [0.01;0.40]
var_cycle_loo 0.06 [0.04;0.07] 0.27 [0.23;0.32] 0.51 [0.47;0.54] NA 0.16 [0.10;0.22] 0.14 [0.08;0.19] 0.04 [-0.01;0.08] NA
var_id 0.27 [0.23;0.31] (10%) 0.19 [0.13;0.26] (20%) 0.30 [0.27;0.33] (50%) 0.66 [0.57;0.77] (55%) 0.27 [0.22;0.31] (34%) 0.35 [0.27;0.43] (27%) 0.08 [0.00;0.22] (3%) 0.31 [0.15;0.46] (48%)
var_cycle 0.03 [0.00;0.07] (0%) 0.16 [0.11;0.21] (13%) 0.14 [0.11;0.17] (12%) NA 0.07 [0.00;0.17] (4%) 0.13 [0.01;0.28] (5%) 0.27 [0.02;0.48] (25%) NA
var_resid 0.81 [0.79;0.83] (90%) 0.35 [0.33;0.37] (68%) 0.26 [0.25;0.27] (38%) 0.59 [0.56;0.61] (45%) 0.40 [0.37;0.42] (62%) 0.55 [0.51;0.60] (68%) 0.51 [0.38;0.63] (72%) 0.32 [0.24;0.42] (52%)
Validity (Forward counting)
r_fc_stirn 0.24 [ 0.21; 0.27] 0.03 [-0.03; 0.09] -0.00 [-0.05; 0.05] 0.07 [ 0.02; 0.12] 0.00 [-0.06; 0.06] -0.05 [-0.15; 0.05] 0.08 [-0.11; 0.26] -0.04 [-0.29; 0.22]
r_log_fc_stirn 0.25 [ 0.22; 0.28] 0.02 [-0.04; 0.08] -0.00 [-0.05; 0.05] 0.05 [ 0.01; 0.10] 0.00 [-0.06; 0.07] -0.00 [-0.11; 0.10] 0.10 [-0.09; 0.27] 0.06 [-0.20; 0.31]
r_diff_fc_stirn 0.26 [ 0.23; 0.29] 0.04 [-0.02; 0.10] -0.01 [-0.06; 0.04] 0.05 [ 0.00; 0.10] 0.04 [-0.03; 0.10] -0.08 [-0.18; 0.02] 0.00 [-0.18; 0.18] -0.13 [-0.37; 0.14]
r_log_diff_fc_stirn 0.26 [ 0.23; 0.29] 0.03 [-0.03; 0.09] -0.02 [-0.07; 0.03] 0.03 [-0.02; 0.08] 0.03 [-0.03; 0.10] -0.01 [-0.11; 0.09] 0.01 [-0.17; 0.20] -0.12 [-0.37; 0.14]
r_loo_fc 0.57 [0.55;0.59] 0.14 [NaN;0.21] NaN [NaN;NaN] NaN [NaN;NaN] NaN [NaN;NaN] NaN [NaN;0.09] NaN [NaN;NaN] NaN [NaN;NaN]
r_fc_imputed 0.57 [ 0.55; 0.59] 0.21 [ 0.15; 0.26] 0.07 [ 0.02; 0.12] 0.10 [ 0.05; 0.15] 0.04 [-0.02; 0.11] 0.10 [-0.01; 0.20] 0.17 [-0.01; 0.34] 0.12 [-0.14; 0.37]
r_diff_fc_imputed 0.63 [ 0.61; 0.65] 0.22 [ 0.16; 0.28] 0.15 [ 0.10; 0.19] 0.17 [ 0.13; 0.22] 0.02 [-0.04; 0.09] 0.12 [ 0.02; 0.22] 0.02 [-0.16; 0.20] -0.05 [-0.31; 0.21]
sd_fc_imputed 0.50 (93%) 0.48 (87%) 0.52 (101%) 0.40 (60%) 0.37 (51%) 0.31 (36%) 0.46 (78%) 0.50 (93%)
r_diff_fc_imputed_rr 0.64 [ 0.62; 0.66] 0.23 [ 0.17; 0.29] 0.15 [ 0.10; 0.19] 0.22 [ 0.16; 0.28] 0.03 [-0.06; 0.12] 0.20 [ 0.04; 0.35] 0.02 [-0.18; 0.23] -0.05 [-0.32; 0.22]
plot_fc
r_fc 0.58 [0.57;0.60] 0.22 [0.17;0.26] 0.12 [0.09;0.16] 0.12 [0.09;0.16] 0.09 [0.02;0.15] 0.14 [0.04;0.23] 0.18 [0.03;0.32] 0.18 [0.03;0.35]
Validity (Backward counting)
r_bc_stirn 0.34 [ 0.31; 0.37] -0.06 [-0.12; 0.01] -0.01 [-0.08; 0.06] 0.08 [ 0.03; 0.13] 0.01 [-0.06; 0.07] 0.03 [-0.06; 0.11] 0.04 [-0.15; 0.23] NA
r_log_bc_stirn 0.36 [ 0.33; 0.39] -0.06 [-0.12; 0.00] -0.00 [-0.08; 0.07] 0.08 [ 0.03; 0.13] 0.01 [-0.05; 0.08] 0.04 [-0.05; 0.12] 0.07 [-0.12; 0.25] NA
r_diff_bc_stirn 0.36 [ 0.33; 0.39] -0.04 [-0.10; 0.02] -0.04 [-0.11; 0.03] 0.08 [ 0.04; 0.13] 0.01 [-0.06; 0.08] -0.04 [-0.13; 0.04] 0.01 [-0.18; 0.20] NA
r_log_diff_bc_stirn 0.38 [ 0.35; 0.40] -0.04 [-0.10; 0.02] -0.04 [-0.11; 0.03] 0.07 [ 0.03; 0.12] 0.00 [-0.06; 0.07] -0.03 [-0.11; 0.05] 0.05 [-0.14; 0.23] NA
r_loo_bc 0.68 [0.66;0.69] 0.13 [NaN;0.20] NaN [NaN;NaN] NaN [NaN;0.07] NaN [NaN;NaN] NaN [NaN;NaN] NaN [NaN;NaN] NA
r_bc_imputed 0.68 [ 0.66; 0.70] 0.15 [ 0.09; 0.21] 0.08 [ 0.01; 0.15] 0.15 [ 0.10; 0.19] 0.03 [-0.03; 0.10] 0.10 [ 0.01; 0.18] 0.12 [-0.07; 0.30] NA
r_diff_bc_imputed 0.73 [ 0.72; 0.75] 0.17 [ 0.11; 0.23] 0.18 [ 0.11; 0.25] 0.21 [ 0.16; 0.25] 0.04 [-0.03; 0.10] 0.12 [ 0.03; 0.20] 0.05 [-0.14; 0.24] NA
sd_bc_imputed 0.59 (86%) 0.50 (63%) 0.51 (64%) 0.48 (58%) 0.50 (63%) 0.40 (41%) 0.53 (71%) NA
r_diff_bc_imputed_rr 0.76 [ 0.74; 0.77] 0.21 [ 0.14; 0.28] 0.22 [ 0.14; 0.30] 0.27 [ 0.21; 0.33] 0.05 [-0.03; 0.13] 0.18 [ 0.05; 0.31] 0.06 [-0.16; 0.28] NA
plot_bc NA
r_bc 0.69 [0.67;0.70] 0.22 [0.17;0.27] 0.13 [0.08;0.17] 0.16 [0.13;0.20] 0.06 [0.01;0.12] 0.11 [0.03;0.18] 0.17 [0.03;0.30] NA
Validity (LH)
r_prob_lh 0.57 [ 0.54; 0.59] NA NA 0.09 [ 0.03; 0.14] 0.05 [-0.03; 0.13] 0.03 [-0.06; 0.13] 0.14 [-0.07; 0.34] NA
r_log_prob_lh 0.54 [ 0.51; 0.56] NA NA 0.10 [ 0.04; 0.16] 0.06 [-0.02; 0.14] 0.06 [-0.04; 0.15] 0.14 [-0.07; 0.34] NA
r_diff_prob_lh 0.61 [ 0.58; 0.63] NA NA 0.12 [ 0.07; 0.18] 0.05 [-0.03; 0.12] -0.01 [-0.11; 0.08] 0.04 [-0.17; 0.24] NA
r_log_diff_prob_lh 0.57 [ 0.55; 0.60] NA NA 0.14 [ 0.08; 0.19] 0.05 [-0.03; 0.13] 0.01 [-0.09; 0.10] 0.05 [-0.16; 0.26] NA
r_loo_lh 0.72 [0.70;0.74] NA NA NaN [NaN;NaN] NaN [NaN;0.10] NaN [NaN;NaN] NaN [NaN;NaN] NA
r_lh_imputed 0.72 [ 0.70; 0.74] NA NA 0.12 [ 0.07; 0.18] 0.10 [ 0.03; 0.18] 0.07 [-0.02; 0.17] 0.10 [-0.11; 0.30] NA
r_diff_lh_imputed 0.77 [ 0.75; 0.78] NA NA 0.18 [ 0.13; 0.24] 0.08 [ 0.00; 0.15] 0.02 [-0.08; 0.11] 0.01 [-0.19; 0.22] NA
sd_lh_imputed 0.60 (85%) NA NA 0.53 (66%) 0.51 (60%) 0.43 (43%) 0.50 (59%) NA
r_diff_lh_imputed_rr 0.79 [ 0.78; 0.81] NA NA 0.22 [ 0.16; 0.29] 0.10 [ 0.00; 0.20] 0.03 [-0.12; 0.17] 0.02 [-0.25; 0.29] NA
plot_lh NA NA NA
r_lh 0.73 [0.72;0.74] NA NA 0.13 [0.10;0.17] 0.14 [0.05;0.22] 0.08 [0.01;0.16] 0.18 [0.03;0.33] NA

Progesterone (P4)

Show code 
summary_table("Progesterone")
Progesterone measures
Comparing salivary and serum measures on a variety of performance criteria
BioCycle Roney '13 OCMATE Marcinkowska '20 Stern '21 Jünger '18 Blake '17 Grebe '16
Study
Dataset BioCycle Roney '13 OCMATE Marcinkowska '20 Stern '21 Jünger '18 Blake '17 Grebe '16
Hormone Progesterone Progesterone Progesterone Progesterone Progesterone Progesterone Progesterone Progesterone
Citation
Wactawski-Wende, J., Schisterman, E. F., Hovey, K. M., Howards, P. P., Browne, R. W., Hediger, M., Liu, A., Trevisan, M., & BioCycle Study Group. (2009) Wactawski-Wende, J., Schisterman, E. F., Hovey, K. M., Howards, P. P., Browne, R. W., Hediger, M., Liu, A., Trevisan, M., & BioCycle Study Group. (2009). BioCycle study: design of the longitudinal study of the oxidative stress and hormone variation during the menstrual cycle. Paediatric and Perinatal Epidemiology, 23(2), 171–184. https://doi.org/10.1111/j.1365-3016.2008.00985.x Howards, P. P., Schisterman, E. F., Wactawski-Wende, J., Reschke, J. E., Frazer, A. A., & Hovey, K. M. (2009). Timing clinic visits to phases of the menstrual cycle by using a fertility monitor: the BioCycle Study. American Journal of Epidemiology, 169(1), 105–112. https://doi.org/10.1093/aje/kwn287 Prasad, A., Mumford, S. L., Buck Louis, G. M., Ahrens, K. A., Sjaarda, L. A., Schliep, K. C., Perkins, N. J., Kissell, K. A., Wactawski-Wende, J., & Schisterman, E. F. (2014). Sexual activity, endogenous reproductive hormones and ovulation in premenopausal women. Hormones and Behavior, 66(2), 330–338. https://doi.org/10.1016/j.yhbeh.2014.06.012
Roney, J. R., & Simmons, Z. L. (2013)Roney, J. R., & Simmons, Z. L. (2013). Hormonal predictors of sexual motivation in natural menstrual cycles. Hormones and Behavior, 63(4), 636–645. https://doi.org/10.1016/j.yhbeh.2013.02.013
Jones, B. C., Hahn, A. C., Fisher, C. I., Wang, H., Kandrik, M., Han, C., Fasolt, V., Morrison, D., Lee, A. J., Holzleitner, I. J., O’Shea, K. J., Roberts, S. C., Little, A. C., & DeBruine, L. M. (2018)Jones, B. C., Hahn, A. C., Fisher, C. I., Wang, H., Kandrik, M., Han, C., Fasolt, V., Morrison, D., Lee, A. J., Holzleitner, I. J., O’Shea, K. J., Roberts, S. C., Little, A. C., & DeBruine, L. M. (2018). No Compelling Evidence that Preferences for Facial Masculinity Track Changes in Women’s Hormonal Status. Psychological Science, 29(6), 996–1005. https://doi.org/10.1177/0956797618760197
Marcinkowska, U. M. (2020)Marcinkowska, U. M. (2020). Importance of Daily Sex Hormone Measurements Within the Menstrual Cycle for Fertility Estimates in Cyclical Shifts Studies. Evolutionary Psychology: An International Journal of Evolutionary Approaches to Psychology and Behavior, 18(1), 1474704919897913. https://doi.org/10.1177/1474704919897913 Marcinkowska, U. M., Ellison, P. T., Galbarczyk, A., Milkowska, K., Pawlowski, B., Thune, I., & Jasienska, G. (2016). Lack of support for relation between woman’s masculinity preference, estradiol level and mating context. Hormones and Behavior, 78, 1–7. https://doi.org/10.1016/j.yhbeh.2015.10.012 Marcinkowska, U. M., Galbarczyk, A., & Jasienska, G. (2018). La donna è mobile? Lack of cyclical shifts in facial symmetry, and facial and body masculinity preferences-A hormone based study. Psychoneuroendocrinology, 88, 47–53. https://doi.org/10.1016/j.psyneuen.2017.11.007 Marcinkowska, U. M., Kaminski, G., Little, A. C., & Jasienska, G. (2018). Average ovarian hormone levels, rather than daily values and their fluctuations, are related to facial preferences among women. Hormones and Behavior, 102, 114–119. https://doi.org/10.1016/j.yhbeh.2018.05.013
Stern, J., Kordsmeyer, T. L., & Penke, L. (2021)Stern, J., Kordsmeyer, T. L., & Penke, L. (2021). A longitudinal evaluation of ovulatory cycle shifts in women’s mate attraction and preferences. Hormones and Behavior, 128, 104916. https://doi.org/10.1016/j.yhbeh.2020.104916
Jünger, J., Kordsmeyer, T. L., Gerlach, T. M., & Penke, L. (2018)Jünger, J., Kordsmeyer, T. L., Gerlach, T. M., & Penke, L. (2018). Fertile women evaluate male bodies as more attractive, regardless of masculinity. Evolution and Human Behavior: Official Journal of the Human Behavior and Evolution Society, 39(4), 412–423. https://doi.org/10.1016/j.evolhumbehav.2018.03.007
Blake, K. R., Bastian, B., O’Dean, S. M., & Denson, T. F. (2017)Blake, K. R., Bastian, B., O’Dean, S. M., & Denson, T. F. (2017). High estradiol and low progesterone are associated with high assertiveness in women. Psychoneuroendocrinology, 75, 91–99. http://www.sciencedirect.com/science/article/pii/S0306453016302190
Grebe, N. M., Emery Thompson, M., & Gangestad, S. W. (2016)Grebe, N. M., Emery Thompson, M., & Gangestad, S. W. (2016). Hormonal predictors of women’s extra-pair vs. in-pair sexual attraction in natural cycles: Implications for extended sexuality. Hormones and Behavior, 78, 211–219. https://doi.org/10.1016/j.yhbeh.2015.11.008
Scheduling Distributed across cycle
More, regularly menstruating premenopausal women for two menstrual cycles. Participants visited the clinic up to 8 times per cycle, at which time blood and urine were collected. The visits occurred at key hormonally defined phases of the menstrual cycle, with the help of an algorithm based on cycle length and data from a fertility monitor. The study included 16 cycle visits (eight per cycle) over two cycles. The timing of these visits was scheduled to correspond with specific times of the menstrual cycle where the most hormonal variation would be expected, approximately days 2, 7, 12, 13, 14, 18, 22 and 27 of a 28-day cycle, adjusted for cycle length (Table 4). Cycle visits were routinely scheduled between 7:00 AM and 8:30 AM to allow for collection of fasting samples and to reduce diurnal variation.
 Every day for up to two cycles.
MoreHere we collected daily saliva samples across 1–2 menstrual cycles from a sample of naturally cycling young women, as well as daily diary reports of both sexual behavior and self-reported sexual desire.
 Irrespective of cycle.
MoreParticipants completed up to three blocks of test sessions (mean time between Block 1 and Block 2 = 230 days; mean time between Block 2 and Block 3 = 487 days). Each of the three blocks of test sessions consisted of five weekly test sessions. Table 1 shows how many women completed one, two, three, four, or five test sessions in Blocks 1 through 3.
 Whole cycle
MoreParticipants were asked to collect saliva samples each morning starting from the first day of menstrual bleeding, until the end of the menstrual cycle (i.e., the day before the onset of the next menstrual bleeding). Due to financial restrictions on average 15 hormonal measurements were done per participant. For E centred around ovulation, for P last 15 days of the cycle.
 peri-ovulatory (2x), mid-luteal und premenstrual
MoreSessions two to five were computer-based testing sessions and took place across different phases of the ovulatory cycle, scheduled based on backward counting and the observed LH test surge. Suitable testing days were computed with the help of an Excel sheet created for that purpose (see open material). All participants completed two sessions in their expected fertile phase (mid to late follicular phase, approx. 5–6 days before ovulation) and two sessions in their expected luteal phase (one session in the mid luteal phase, one session in the premenstrual phase). Scheduling was validated via LH test results and via following up to the day of the next menstrual onset. Details can be found in the supple- mentary material. The starting session for each participant depended on their current cycle phase at the introductory session and their personal schedule. Of all participants who finished all sessions, 134 participants started with the first session in their fertile phase, and 123 started in the luteal phase.
 Peri-ovulatory & Luteal
MoreSessions two to five, the computer-based testing sessions, took place across two ovulatory cycles per participant, once per cycle during the fertile and once during the luteal phase. To control for possible effects of diurnal changes in hormone levels (Bao et al., 2003; Veldhuis et al., 1988), all sessions took place in the second half of the day (mainly between 11.30 am and 6 pm).
 Fertile and non-fertile phase.
MoreThe repeated design of the study required that all participants attended one fertile and one non-fertile laboratory session. Hence, all participants included here detected a LH surge and thus had ovulatory cycles. Forty-seven percent of non-fertile sessions occurred in the early follicular phase (mean cycle day = 4.02, SD = 2.54) and the remain- der occurred in the late luteal phase (mean cycle day=26.06, SD=3.45). Most participants completed both fertile and non- fertile sessions in the same menstrual cycle (63.5%), the remainder attended sessions occurring 1, 2, or 3 cycles apart. Once a positive LH surge was detected, 85.4% of participants attended their fertile session within one day, the remaining 14.6% attended this session within two days. Following a surge in LH, ovulation is expected to occur approximately 24–48 h later (Direito et al., 2013).2
 Irrespective of cycle phase.
MoreWe do emphasize that, although we sought to representatively sample days and phases of the cycle, by chance the luteal phase appears to have been overrepresented in our sample.
Measure
Method Serum CL-EIA (chemiluminescence enzyme immunoassay) Salivary radioimmunoassay (Siemens Health Diagnostics) Salivary immunoassay (Salimetrics) Salivary immunoassay (DRG) Salivary liquid chromatography–mass spectrometry Salivary liquid chromatography–mass spectrometry Salivary immunoassay (IBL) Salivary immunoassay (Salimetrics)
Measure LOD=200.00, LOQ=NA, Intraassay CV=NA, Interassay CV=<10 LOD=9.14, LOQ=NA, Intraassay CV=4.57, Interassay CV=7.36 LOD=5.00, LOQ=10.00, Intraassay CV=6.20, Interassay CV=7.55 LOD=2.50, LOQ=10.00, Intraassay CV=4.9%, Interassay CV=14.1% LOD=NA, LOQ=5.00, Intraassay CV=10.80, Interassay CV=9.70 LOD=NA, LOQ=5.00, Intraassay CV=10.80, Interassay CV=9.70 LOD=8.90, LOQ=NA, Intraassay CV=<12, Interassay CV=<12 LOD=5.00, LOQ=10.00, Intraassay CV=14.52, Interassay CV=4.83
Procedure
MoreBlood collection and handling protocols were designed to minimize variability (Wactawski- Wende et al., 2009). All samples were processed and frozen at −80°C within 90 minutes of phlebotomy and analytes were measured in participant-specific batches within a single run to limit analytical variability. Estradiol, LH, follicle-stimulating hormone (FSH), and progesterone concentrations were measured in serum samples using solid-phase competitive chemiluminescent enzymatic immunoassays (DPC Immulite 2000 analyzer, Siemens Medical Solutions Diagnostics, Deerfield, IL) at the Kaleida Health Center for Laboratory Medicine (Buffalo, NY).
MoreWomen were also instructed to collect a saliva sample each morn- ing. They were asked to do so at least 30 min after any eating or drinking, ideally upon first waking. Collection was via passive drool (a few minutes after rinsing with clean water) into pre-labeled poly- propylene vials. Women stored these vials in home freezers and then delivered them weekly to our research lab, at which time they were given a new batch of vials. Saliva samples were then stored at − 80 C until being shipped for assay. Saliva samples were shipped on dry ice to the Endocrine Core Laboratory at the California Regional Primate Research Center, Davis, CA. Samples were centrifuged at 3000 rpm for 20 min. to separate the aqueous component from other particles. Concentrations of progester- one were estimated in duplicate using commercial radioimmunoassay kits (Siemens Health Diagnostics, Inc., Los Angeles, CA). Assay proce- dures were modified to accommodate overall lower levels of progester- one in human saliva relative to plasma as follows: 1) standards were diluted to concentrations ranging from 0.05 to 4.0 ng/mL; and 2) sample volume was increased to 200 μl. The progesterone assay has a least detectable dose of 0.00914 ng/ml; intra- and inter-assay CVs were 4.57% and 7.36%, respectively.
MoreParticipants provided a saliva sample via passive drool (Papacosta & Nassis, 2011) in each test session. Partici- pants were instructed to avoid consuming alcohol and coffee in the 12 hr prior to participation and avoid eating, smoking, drinking, chewing gum, or brushing their teeth in the 60 min prior to participation. Each woman’s test sessions took place at approximately the same time of day to minimize effects of diurnal changes in hormone levels (Bao et al., 2003; Veldhuis et al., 1988). Saliva samples were frozen immediately and stored at −32° C until being shipped on dry ice to the Salimet- rics Lab (Suffolk, United Kingdom) for analysis, where they were assayed using the Salivary Progesterone Enzyme Immunoassay Kit 1-1502 (M = 148.55 pg/mL, SD = 96.13 pg/mL, sen- sitivity = 5 pg/mL, intra-assay CV = 6.20%, inter-assay CV = 7.55%).
MoreParticipants were asked to collect saliva samples each morning starting from the first day of menstrual bleeding, until the end of the menstrual cycle (i.e., the day before the onset of the next menstrual bleeding). Women were verbally instructed by the principal investigator concerning collecting and storing the saliva and were given a set of 2-ml centrifuge tubes with the minimum amount of required saliva marked on a tube together with written instructions. Saliva samples were collected in the morning, preferably before eating, drinking, or smoking, or not earlier than 30 min after eating, drinking, or smoking. Immediately after collec- tion, samples were frozen in the participant’s home freezer. After the end of the cycle, samples were transported in portable freezers to the laboratory where hormonal assays for measure- ments of 17-b-estradiol (E2) and 17-a-hydroxy-progesterone (P) were conducted. Hormonal measurements were conducted using commercially available hormonal assays of DRG Inter- national, Inc. ELISA plates SLV4188 (sensitivity: 0.4 pg/ml, standard range: 1–100 pg/ml) for E2 and SLV3140 for P (sen- sitivity: 2.5 pg/ml, standard range: 10–5,000 pg/ml). All sam- ples were assayed in duplicates. The quality of hormonal measurements was monitored for each plate separately by including samples of known concentrations with low and high E2 and P levels. Inter-assay variability was 10.1%, intra-assay variability was 7.5% for E2 and 14.1% and 4.9% for P, which is within acceptable values for steroid sex hormones assays (Schultheiss & Stanton, 2009).
MoreFor hormone assays, we collected four saliva samples from each participant (one per testing session). Contamination of saliva samples was minimized by asking participants to abstain from eating, drinking (except plain water), smoking, chewing gum, or brushing teeth for at least one hour before each session. The samples were stored at − 80 ◦C directly after collection until shipment on dry ice to the Kirschbaum Lab at Technical University of Dresden, Germany (one freeze-thaw cycle), where progesterone was assessed via liquid chromatography mass spectrometry (LCMS, Gao et al., 2015). Liquid to liquid extraction was carried out by adding 20 μL internal stan- dard and 1 mL ethyl acetate to 400 μL saliva in a 2 mL polypropylene tube. The resulting mixture was subsequently rotated for 1 min on the vortex and then centrifuged for 10 min at 12000 r/min with centrifuge (Hettich, MIKRO 22 R). The ethyl acetate layer was transported to a new glass tube and evaporated to dryness under nitrogen. The residue was resuspended in 120 μL methanol/ water in a ratio of 50:50 (v/v), 50 μL of which was injected into the LC-MS/MS system. The LC-MS/MS system consisted of Shimadzu HPLC system, and AB Sciex Triple Quad 6500+ System equipped with the electrospray ionization (ESI) source. See Gao et al. (2015) for more details.
MoreNext, the saliva samples were collected via passive drool. For hormone assays, we collected four saliva samples from each participant (one per testing session). Contamination of saliva samples was minimized by asking participants to abstain from eating, drinking (except plain water), smoking, chewing gum or brushing teeth for at least one hour before each session. The samples were stored at -80°C directly after collection until shipment on dry ice to the Kirschbaum Lab at Technical University of Dresden, Germany, where estradiol, progesterone, testosterone and cortisol was assessed via liquid chromatography mass spectrometry (LCMS; Gao, Stalder, & Kirschbaum, 2015). Since the LCMS analysis of the estradiol levels did only detect 22% of all possible values, the samples were reanalyzed using the highly sensitive 17β-estradiol enzyme immunoassay kit (IBL International, Hamburg, Germany). These latter estradiol values were used in subsequent analyses.
MoreProgesterone and estradiol samples were stored at −20◦C and analyzed by a professional reference laboratory in Dresden, Germany. After thawing, samples were centrifuged at 3000 rpm for five minutes, which resulted in a clear supernatant of low viscosity. Salivary progesterone and estradiol concentrations were measured using commercially available chemiluminescence-immuno-assays with high sensitivity (IBL International, Hamburg, Germany). Intra- and inter-assay coefficients of variations for both hormones were below 12%.
MoreWe asked participants to provide saliva samples (~5 mL) via passive drool, from which we assayed estradiol, progesterone, and testosterone twice during the study: once during an initial session, and once upon awakening the day of a follow-up session one week later. Saliva samples were pipetted from a cup to test tubes, then frozen at -20° C. Prior to assays, frozen samples were thawed, mixed by vortexing, then centrifuged for 15 minutes to break up and precipitate mucins. Salivary 17β estradiol, progesterone, and testosterone concentrations were determined using enzyme-linked immunosorbent assays (ELISA) designed for saliva by Salimetrics LLC (Carlsbad, CA).
LH_test
MoreParticipants were also provided with and trained to use the Clearblue Easy Fertility Monitor (Inverness Medical Innovations, Inc., Waltham, Massachusetts) (www.clearblueeasy.com). The fertility monitor was originally developed to assist women in becoming pregnant by helping them to identify their fertile window through measurement of both estrone- 3-glucuronide (E3G) and LH in urine as described in greater detail elsewhere (13). Briefly, the monitor is synchronized to a woman’s cycle and then is checked daily to see whether a test is requested. Between the sixth and the ninth days of a woman’s cycle (depending on her cycle length history), the monitor begins to request daily tests for 10 days. On test days, the woman briefly submerges a test stick in her first- morning urine and then inserts the test stick into the monitor. The test stick has a nitrocellulose strip with an anti-LH antibody zone and an E3G conjugate zone. The monitor optically reads the level of E3G and LH in the urine by the intensity of the lines in the corresponding zones (13). Each day, the monitor assigns the woman to low, high, or peak fertility on the basis of her E3G and LH levels. Thus, unlike home LH test sticks, the monitor provides informa- tion to help anticipate the LH surge.
MoreAll participants received LH kits. Each kit consisted of sterilized urine cups and 10 LH tests. Women were instructed to conduct the tests from the 10th until the 20th day of the cycle or until a positive result was obtained.
Morehighly sensitive (10mIU) LH urine ovulation test strips from MedNet GmbH. Participants started LH-testing after menstruation (around reverse cycle day 21) and continued until a rise of LH (positive tests) was observed and a minimum of two days after the tests were negative again (as suggested by Roney, 2018b). Participants were provided with a minimum of ten LH tests each and provided daily pictures of the tests to the investigators for confir- mation. LH results were used to allow flexible scheduling in case LH test results differed from the scheduling based on counting (see supplementary material for details).
Morehighly sensitive (10 mIU/ml) urine ovulation test strips from purbay®, which measure the luteinizing hormone (LH). These LH-tests had to be done at home at the estimated day of ovulation and the four days prior to that and results were self- reported by the participants.
MoreTo maximize the likelihood of detecting surges in LH, we fol- lowed the standardized LH testing procedure in Blake et al. (2016b). All participants used commercially available urinary LH tests (Blue Cross Bio-Medical Co. LTD, CE/FDA Registered) and tested until a positive surge was detected. Wave 1 participants tested until a positive result was reported (a maximum of 10days) and Wave 2 tested for 11 continuous days, even if a surge was reported ear- lier. We instructed participants to test between 1000 h and 2000 h and report the result to the research team via SMS or email. The repeated design of the study required that all participants attended one fertile and one non-fertile laboratory session. Hence, all participants included here detected a LH surge and thus had ovulatory cycles. Participants who did not record a surge in LH were not eligible for inclusion in the study (see Blake et al., 2016b for full details of the sampling procedure).
Sample
Age 27.3±8.21 NA 21.5±3.29 NA 23.1±3.28 23.2±3.45 22.7±4.87 NA
In Relationship 25% NA 36% NA 47% 48% 53% NA
Cycle length 28.8±4.10 27.6±5.06 29.7±6.73 28.2±2.99 30.0±4.75 29.5±6.54 29.2±2.50 28.8±3.71
Women 259 43 384 102 257 157 60 33
Cycles 509 122 907 102 454 398 109 33
Days 4078 2367 2394 2265 1028 628 120 66
% women no LH surge 43/257 (17%) NA NA 25/99 (25%) 50/238 (21%) 10/124 (8%) 0/58 (0%) NA
% cycles no LH surge 141/481 (29%) NA NA 25/99 (25%) 134/377 (36%) 59/240 (25%) 0/103 (0%) NA
Usable n women 257 42 360 99 238 156 58 31
Usable n days 3682 1121 1664 1550 778 537 114 57
Descriptive statistics
Distribution
logmean 7.24 3.76 4.81 4.26 2.26 2.87 4.77 3.88
logsd 1.36 0.67 0.76 1.03 1.49 1.59 0.88 0.91
missing 177 (4%) 1139 (48%) 103 (4%) 686 (30%) 160 (16%) 47 (7%) 6 (5%) 5 (8%)
outliers 0 (0%) 0 (0%) 1 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 0 (0%)
censored 147 (4%) 33 (1%) 4 (0%) 15 (1%) 0 (0%) 0 (0%) 0 (0%) 1 (2%)
mean 3437.53 53.74 158.54 106.34 27.97 53.72 170.22 69.62
sd 4683.28 39.21 121.31 88.95 52.67 91.17 155.69 62.44
median 1000.00 40.36 130.17 85.60 7.45 13.50 112.00 49.28
mad 1037.82 22.78 90.32 71.63 8.55 16.12 91.11 39.70
range 200.00, 27700.00 9.14, 310.00 5.00, 1859.40 2.50, 875.96 0.22, 671.77 0.26, 1480.00 14.13, 748.71 5.00, 293.46
Interindividual differences
var_id_loo 0.02 [0.01;0.02] 0.09 [0.06;0.12] 0.32 [0.28;0.36] 0.45 [0.41;0.49] -0.00 [-0.01;-0.00] -0.01 [-0.01;-0.00] -0.00 [-0.06;0.04] 0.11 [-0.05;0.25]
var_cycle_loo 0.02 [0.01;0.02] 0.10 [0.07;0.14] 0.32 [0.28;0.36] NA -0.00 [-0.01;0.00] -0.01 [-0.02;-0.01] -0.01 [-0.07;0.03] NA
var_id 0.30 [0.23;0.37] (5%) 0.19 [0.06;0.29] (8%) 0.49 [0.43;0.55] (39%) 0.73 [0.62;0.85] (49%) 0.10 [0.00;0.27] (1%) 0.11 [0.00;0.28] (1%) 0.26 [0.02;0.55] (10%) 0.58 [0.13;0.96] (37%)
var_cycle 0.05 [0.00;0.13] (0%) 0.19 [0.10;0.28] (8%) 0.18 [0.04;0.27] (6%) NA 0.27 [0.02;0.53] (4%) 0.09 [0.00;0.25] (0%) 0.25 [0.01;0.60] (11%) NA
var_resid 1.37 [1.34;1.41] (95%) 0.63 [0.61;0.66] (84%) 0.58 [0.56;0.61] (55%) 0.73 [0.70;0.76] (51%) 1.46 [1.38;1.55] (95%) 1.59 [1.50;1.69] (99%) 0.80 [0.64;0.96] (79%) 0.75 [0.56;1.00] (63%)
Validity (Forward counting)
r_fc_stirn -0.35 [-0.37;-0.32] -0.32 [-0.37;-0.27] -0.18 [-0.23;-0.13] -0.07 [-0.12;-0.02] -0.32 [-0.38;-0.25] -0.54 [-0.61;-0.46] -0.13 [-0.31; 0.05] -0.20 [-0.44; 0.06]
r_log_fc_stirn -0.34 [-0.37;-0.31] -0.33 [-0.38;-0.27] -0.17 [-0.22;-0.12] -0.05 [-0.10;-0.00] -0.52 [-0.57;-0.47] -0.65 [-0.71;-0.59] -0.11 [-0.29; 0.07] -0.19 [-0.43; 0.08]
r_diff_fc_stirn -0.37 [-0.40;-0.34] -0.34 [-0.39;-0.29] -0.23 [-0.27;-0.18] -0.18 [-0.23;-0.13] -0.27 [-0.33;-0.20] -0.51 [-0.58;-0.43] -0.02 [-0.20; 0.16] -0.06 [-0.32; 0.20]
r_log_diff_fc_stirn -0.36 [-0.39;-0.33] -0.35 [-0.41;-0.30] -0.24 [-0.28;-0.19] -0.18 [-0.22;-0.13] -0.41 [-0.47;-0.35] -0.60 [-0.67;-0.53] 0.00 [-0.18; 0.19] -0.10 [-0.35; 0.17]
r_loo_fc 0.72 [0.70;0.74] 0.48 [0.43;0.52] 0.26 [0.20;0.31] 0.02 [NaN;0.18] 0.62 [0.57;0.66] 0.70 [0.64;0.74] NaN [NaN;0.24] NaN [NaN;NaN]
r_fc_imputed 0.72 [ 0.71; 0.74] 0.48 [ 0.44; 0.53] 0.29 [ 0.24; 0.34] 0.19 [ 0.14; 0.23] 0.62 [ 0.57; 0.66] 0.67 [ 0.61; 0.72] 0.20 [ 0.02; 0.37] 0.16 [-0.11; 0.40]
r_diff_fc_imputed 0.77 [ 0.75; 0.78] 0.53 [ 0.49; 0.57] 0.33 [ 0.28; 0.37] 0.37 [ 0.33; 0.42] 0.44 [ 0.39; 0.50] 0.61 [ 0.54; 0.67] -0.19 [-0.36;-0.01] 0.09 [-0.17; 0.34]
sd_fc_imputed 1.07 (84%) 1.06 (82%) 1.13 (92%) 0.96 (67%) 1.11 (89%) 1.05 (81%) 0.92 (61%) 1.14 (95%)
r_diff_fc_imputed_rr 0.80 [ 0.78; 0.81] 0.57 [ 0.53; 0.61] 0.34 [ 0.29; 0.39] 0.44 [ 0.39; 0.49] 0.47 [ 0.41; 0.52] 0.65 [ 0.58; 0.71] -0.24 [-0.44;-0.01] 0.09 [-0.18; 0.35]
plot_fc
r_fc 0.74 [0.74;0.75] 0.53 [0.49;0.56] 0.31 [0.27;0.34] 0.30 [0.27;0.33] 0.63 [0.60;0.66] 0.71 [0.68;0.74] 0.25 [0.07;0.39] 0.20 [0.03;0.38]
Validity (Backward counting)
r_bc_stirn -0.40 [-0.43;-0.37] -0.36 [-0.41;-0.31] -0.25 [-0.32;-0.18] -0.15 [-0.20;-0.10] -0.30 [-0.36;-0.23] -0.42 [-0.49;-0.35] -0.21 [-0.38;-0.02] NA
r_log_bc_stirn -0.41 [-0.44;-0.38] -0.36 [-0.41;-0.30] -0.27 [-0.33;-0.20] -0.13 [-0.18;-0.08] -0.51 [-0.56;-0.45] -0.67 [-0.72;-0.62] -0.17 [-0.35; 0.01] NA
r_diff_bc_stirn -0.42 [-0.45;-0.39] -0.40 [-0.45;-0.35] -0.32 [-0.38;-0.25] -0.27 [-0.32;-0.22] -0.30 [-0.36;-0.23] -0.46 [-0.52;-0.39] -0.05 [-0.24; 0.14] NA
r_log_diff_bc_stirn -0.42 [-0.45;-0.40] -0.40 [-0.45;-0.35] -0.35 [-0.41;-0.29] -0.26 [-0.31;-0.21] -0.43 [-0.49;-0.37] -0.66 [-0.70;-0.61] -0.08 [-0.26; 0.11] NA
r_loo_bc 0.83 [0.81;0.84] 0.47 [0.42;0.52] 0.37 [0.30;0.43] 0.23 [0.14;0.30] 0.67 [0.62;0.71] 0.73 [0.68;0.77] NaN [NaN;0.23] NA
r_bc_imputed 0.83 [ 0.82; 0.84] 0.44 [ 0.40; 0.49] 0.40 [ 0.34; 0.46] 0.28 [ 0.24; 0.33] 0.67 [ 0.63; 0.71] 0.74 [ 0.69; 0.77] 0.25 [ 0.07; 0.42] NA
r_diff_bc_imputed 0.85 [ 0.84; 0.86] 0.51 [ 0.47; 0.56] 0.46 [ 0.41; 0.52] 0.48 [ 0.44; 0.52] 0.55 [ 0.50; 0.60] 0.68 [ 0.63; 0.72] -0.16 [-0.34; 0.03] NA
sd_bc_imputed 1.17 (87%) 1.20 (91%) 1.20 (91%) 1.03 (66%) 1.26 (101%) 1.27 (101%) 1.15 (83%) NA
r_diff_bc_imputed_rr 0.87 [ 0.86; 0.88] 0.53 [ 0.48; 0.57] 0.48 [ 0.42; 0.54] 0.56 [ 0.52; 0.60] 0.55 [ 0.50; 0.60] 0.68 [ 0.63; 0.72] -0.18 [-0.37; 0.03] NA
plot_bc NA
r_bc 0.84 [0.83;0.84] 0.52 [0.48;0.55] 0.40 [0.36;0.45] 0.36 [0.34;0.39] 0.68 [0.66;0.71] 0.75 [0.72;0.77] 0.26 [0.08;0.41] NA
Validity (LH)
r_prob_lh -0.37 [-0.40;-0.33] NA NA -0.16 [-0.22;-0.10] -0.30 [-0.37;-0.22] -0.38 [-0.46;-0.29] -0.18 [-0.37; 0.03] NA
r_log_prob_lh -0.30 [-0.34;-0.26] NA NA -0.13 [-0.18;-0.07] -0.37 [-0.44;-0.30] -0.60 [-0.66;-0.54] -0.19 [-0.38; 0.02] NA
r_diff_prob_lh -0.35 [-0.39;-0.32] NA NA -0.20 [-0.25;-0.14] -0.28 [-0.36;-0.21] -0.44 [-0.51;-0.36] -0.09 [-0.29; 0.12] NA
r_log_diff_prob_lh -0.29 [-0.32;-0.25] NA NA -0.19 [-0.24;-0.13] -0.37 [-0.44;-0.29] -0.63 [-0.69;-0.57] -0.15 [-0.35; 0.06] NA
r_loo_lh 0.87 [0.86;0.88] NA NA 0.24 [0.14;0.31] 0.68 [0.62;0.73] 0.69 [0.63;0.74] NaN [NaN;NaN] NA
r_lh_imputed 0.87 [ 0.86; 0.88] NA NA 0.31 [ 0.25; 0.36] 0.68 [ 0.63; 0.72] 0.62 [ 0.56; 0.68] 0.21 [ 0.00; 0.40] NA
r_diff_lh_imputed 0.88 [ 0.87; 0.88] NA NA 0.44 [ 0.39; 0.49] 0.61 [ 0.55; 0.66] 0.64 [ 0.58; 0.70] 0.06 [-0.15; 0.26] NA
sd_lh_imputed 1.21 (82%) NA NA 1.10 (67%) 1.34 (100%) 1.09 (66%) 1.17 (77%) NA
r_diff_lh_imputed_rr 0.89 [ 0.89; 0.90] NA NA 0.52 [ 0.46; 0.56] 0.61 [ 0.55; 0.66] 0.72 [ 0.66; 0.77] 0.07 [-0.17; 0.30] NA
plot_lh NA NA NA
r_lh 0.88 [0.87;0.88] NA NA 0.34 [0.31;0.37] 0.69 [0.65;0.71] 0.70 [0.67;0.73] 0.24 [0.07;0.38] NA

Estradiol & Progesterone (E2/P4)

Show code 
ratio_summary_table()
Estradiol and progesterone measures
Comparing salivary and serum measures on a variety of performance criteria
BioCycle Blake '17 Stern '21 Jünger '18 Grebe '16 Marcinkowska '20 OCMATE Roney '13
Measure
Dataset BioCycle Blake '17 Stern '21 Jünger '18 Grebe '16 Marcinkowska '20 OCMATE Roney '13
Method Serum Immunoassay Salivary immunoassay (IBL) Salivary Immunoassay Salivary Immunoassay Salivary Immunoassay Salivary immunoassay (DRG) Salivary Immunoassay Salivary Immunoassay
Hormone Estradiol & Progesterone Estradiol & Progesterone Estradiol & Progesterone Estradiol & Progesterone Estradiol & Progesterone Estradiol & Progesterone Estradiol & Progesterone Estradiol & Progesterone
Sample size
Women 259 60 157 257 33 102 384 43
Cycles 509 109 398 454 33 102 907 122
Days 4078 120 628 1028 66 2265 2394 2367
Usable n 3682 114 512 777 57 1034 1664 1091
Relationship
plot_scatter
r_ep 0.14 [ 0.11; 0.17] 0.33 [ 0.16; 0.48] 0.02 [-0.06; 0.11] 0.02 [-0.05; 0.09] 0.45 [ 0.23; 0.63] 0.34 [ 0.29; 0.40] 0.27 [ 0.23; 0.30] 0.19 [ 0.14; 0.25]
r_log_ep 0.28 [ 0.25; 0.31] 0.30 [ 0.12; 0.46] 0.03 [-0.06; 0.11] 0.04 [-0.02; 0.11] 0.55 [ 0.35; 0.70] 0.31 [ 0.26; 0.37] 0.43 [ 0.40; 0.46] 0.19 [ 0.13; 0.24]
r_e_ratio 0.59 [ 0.57; 0.61] 0.30 [ 0.13; 0.46] 0.29 [ 0.21; 0.36] 0.29 [ 0.23; 0.35] -0.07 [-0.32; 0.19] 0.17 [ 0.11; 0.23] 0.13 [ 0.09; 0.17] 0.35 [ 0.30; 0.40]
r_log_e_ratio 0.33 [ 0.30; 0.36] 0.35 [ 0.17; 0.50] 0.37 [ 0.29; 0.44] 0.24 [ 0.17; 0.30] -0.10 [-0.34; 0.16] 0.47 [ 0.42; 0.51] 0.12 [ 0.08; 0.16] 0.41 [ 0.36; 0.46]
r_p_ratio -0.35 [-0.38;-0.32] -0.48 [-0.61;-0.33] -0.26 [-0.33;-0.18] -0.30 [-0.36;-0.24] -0.46 [-0.64;-0.24] -0.30 [-0.35;-0.24] -0.43 [-0.46;-0.39] -0.51 [-0.55;-0.47]
r_log_p_ratio -0.81 [-0.82;-0.80] -0.79 [-0.85;-0.71] -0.92 [-0.93;-0.91] -0.96 [-0.96;-0.95] -0.88 [-0.93;-0.81] -0.69 [-0.72;-0.66] -0.84 [-0.85;-0.83] -0.82 [-0.84;-0.80]
r_bc_ratio 0.53 [ 0.50; 0.55] 0.10 [-0.09; 0.28] 0.36 [ 0.28; 0.43] 0.33 [ 0.26; 0.39] NA 0.14 [ 0.08; 0.20] 0.18 [ 0.11; 0.25] 0.20 [ 0.14; 0.26]
r_bc_log_ratio 0.61 [ 0.59; 0.63] 0.22 [ 0.03; 0.39] 0.65 [ 0.59; 0.69] 0.50 [ 0.45; 0.55] NA 0.24 [ 0.18; 0.30] 0.30 [ 0.23; 0.36] 0.29 [ 0.24; 0.35]
r_erat_bc 0.63 [0.61;0.65] 0.06 [NaN;0.28] 0.68 [0.63;0.72] 0.50 [0.45;0.55] NA 0.23 [0.16;0.28] 0.29 [0.21;0.34] 0.35 [0.29;0.39]
r_loo_bc 0.72 [0.70;0.74] NaN [NaN;0.27] 0.69 [0.63;0.73] 0.51 [0.46;0.56] NA 0.29 [0.22;0.34] 0.29 [0.22;0.35] 0.38 [0.32;0.42]
r_fc_ratio 0.41 [ 0.38; 0.44] 0.08 [-0.11; 0.26] 0.35 [ 0.25; 0.44] 0.36 [ 0.30; 0.42] 0.17 [-0.09; 0.42] 0.06 [ 0.00; 0.12] 0.10 [ 0.05; 0.15] 0.25 [ 0.20; 0.31]
r_fc_log_ratio 0.48 [ 0.46; 0.51] 0.17 [-0.02; 0.34] 0.61 [ 0.54; 0.67] 0.51 [ 0.46; 0.56] 0.26 [-0.00; 0.49] 0.14 [ 0.08; 0.20] 0.18 [ 0.14; 0.23] 0.31 [ 0.26; 0.37]
r_erat_fc 0.49 [0.46;0.51] NaN [NaN;0.21] 0.64 [0.58;0.70] 0.52 [0.47;0.56] NaN [NaN;0.34] 0.12 [NaN;0.18] 0.18 [0.12;0.22] 0.33 [0.28;0.38]
r_loo_fc 0.58 [0.56;0.60] NaN [NaN;0.22] 0.65 [0.58;0.70] 0.53 [0.47;0.57] NaN [NaN;0.36] 0.21 [0.13;0.27] 0.18 [0.12;0.22] 0.35 [0.30;0.40]
r_lh_ratio 0.53 [ 0.50; 0.56] 0.21 [ 0.00; 0.40] 0.27 [ 0.17; 0.36] 0.22 [ 0.14; 0.30] NA 0.12 [ 0.05; 0.19] NA NA
r_lh_log_ratio 0.60 [ 0.57; 0.62] 0.27 [ 0.06; 0.45] 0.58 [ 0.51; 0.64] 0.38 [ 0.31; 0.45] NA 0.20 [ 0.13; 0.27] NA NA
r_erat_lh 0.69 [0.66;0.71] 0.13 [NaN;0.36] 0.61 [0.54;0.66] 0.37 [0.31;0.43] NA 0.18 [0.07;0.25] NA NA
r_loo_lh 0.83 [0.81;0.85] NaN [NaN;0.33] 0.62 [0.55;0.67] 0.39 [0.32;0.44] NA 0.24 [0.15;0.31] NA NA

Methods / Table legend

Literature survey of estradiol assays

We performed the following search on SCOPUS. Journal: Psychoneuroendocrinology or Hormones and Behavior In Abstract Title or Keywords: Menstrual Cycle, Estradiol Limited to articles published 2017 or later.

( SRCTITLE ( psychoneuroendocrinology )  OR  
  SRCTITLE ( hormones  AND  behavior )  AND  
  TITLE-ABS-KEY ( estradiol )  AND  
  TITLE-ABS-KEY ( menstrual  AND  cycle ) )  
  AND  ( LIMIT-TO ( PUBYEAR ,  2022 )  OR  
         LIMIT-TO ( PUBYEAR ,  2021 )  OR  
         LIMIT-TO ( PUBYEAR ,  2020 )  OR  
         LIMIT-TO ( PUBYEAR ,  2019 )  OR  
         LIMIT-TO ( PUBYEAR ,  2018 )  OR  
         LIMIT-TO ( PUBYEAR ,  2017 ) )

We then examined the fulltext of each article to code the following information:

Show code 
lit <- rio::import("lit_search_estradiol.xlsx")
lit <- lit %>% filter(Year >= 2017)
table(lit$`Menstrual Cycle`)


                                              no 
                                               4 
                                   not empirical 
                                              10 
                                      not humans 
                                               1 
sort of (between-subjects study on irregularity) 
                                               1 
                                             yes 
                                              42
Show code 
litmc <- lit %>% filter(`Menstrual Cycle` == "yes")
table(litmc$`Source title`)


   Hormones and Behavior Psychoneuroendocrinology 
                      15                       27
Show code 
saliva <- sum(litmc$`Body Fluid` == "Saliva")
table(litmc$`Body Fluid`)


app-based    Saliva     Serum     Urine 
        1        38         2         1
Show code 
litsal <- litmc %>% filter(`Body Fluid` == "Saliva", coalesce(Notes,"") != "prog only")
table(litsal$`Source title`)


   Hormones and Behavior Psychoneuroendocrinology 
                      15                       22
Show code 
table(litsal$Assay) %>% sort()


Diagnostic Systems Laboratories, Webster 
                                       1 
                             immunoassay 
                                       1 
                      LCMS/MS Kirschbaum 
                                       1 
           unclear (lab: SwisshealthMed) 
                                       1 
                   Demeditec Diagnostics 
                                       2 
                  Labor Diagnostika Nord 
                                       2 
                                     DRG 
                                       6 
                                     IBL 
                                      11 
                             Salimetrics 
                                      12

We found N=58 papers. Of those, n=42 papers were empirical primary studies with at least one research question related to menstrual cycle changes within-women. Of those, n=38 collected saliva. The estradiol immunoassays studied here, i.e. Salimetrics, IBL, and DRG immunoassays accounted for 78% of the assays performed on salivary estradiol (ns=12,11,6). In most cases, the progesterone assay was done using kits from the same company.

Show code 
library(gtsummary)
litsal %>% select(Assay) %>% tbl_summary(sort = list(everything() ~ "frequency"))
Characteristic N = 371
Assay
Salimetrics 12 (32%)
IBL 11 (30%)
DRG 6 (16%)
Demeditec Diagnostics 2 (5.4%)
Labor Diagnostika Nord 2 (5.4%)
Diagnostic Systems Laboratories, Webster 1 (2.7%)
immunoassay 1 (2.7%)
LCMS/MS Kirschbaum 1 (2.7%)
unclear (lab: SwisshealthMed) 1 (2.7%)

1 n (%)

List of papers
Show code 
lit %>% select(Authors, Title, `Source title`, DOI, `Menstrual Cycle`, `Body Fluid`, `Assay`, Notes) %>% gt()
Authors Title Source title DOI Menstrual Cycle Body Fluid Assay Notes
Wang H., Li J., Chen L., He L. Do women's natural hormonal fluctuations modulate prosociality? A within-subject analysis Psychoneuroendocrinology 10.1016/j.psyneuen.2022.105663 yes Saliva DRG NA
Marful A., Paolieri D., Bernal A. Sex, menstrual cycle, and hormonal contraceptives influences on global–local processing Psychoneuroendocrinology 10.1016/j.psyneuen.2021.105430 yes Saliva Labor Diagnostika Nord NA
Rehbein E., Kogler L., Hornung J., Morawetz C., Bayer J., Krylova M., Sundström-Poromaa I., Derntl B. Estradiol administration modulates neural emotion regulation Psychoneuroendocrinology 10.1016/j.psyneuen.2021.105425 no Serum immunoassay NA
Strojny J., Domes G., Fischbacher U., von Dawans B. The modulation of social behavior and empathy via oral contraceptives and female sex hormones Psychoneuroendocrinology 10.1016/j.psyneuen.2021.105250 yes Saliva Demeditec Diagnostics NA
Gloe L.M., Kashy D.A., Jacobs E.G., Klump K.L., Moser J.S. Examining the role of ovarian hormones in the association between worry and working memory across the menstrual cycle Psychoneuroendocrinology 10.1016/j.psyneuen.2021.105285 yes Saliva Salimetrics NA
Gordon J.L., Halleran M., Beshai S., Eisenlohr-Moul T.A., Frederick J., Campbell T.S. Endocrine and psychosocial moderators of mindfulness-based stress reduction for the prevention of perimenopausal depressive symptoms: A randomized controlled trial Psychoneuroendocrinology 10.1016/j.psyneuen.2021.105277 yes Urine immunoassay NA
Gamsakhurdashvili D., Antov M.I., Stockhorst U. Sex-hormone status and emotional processing in healthy women Psychoneuroendocrinology 10.1016/j.psyneuen.2021.105258 yes Saliva IBL NA
Leeners B., Krüger T., Geraedts K., Tronci E., Mancini T., Ille F., Egli M., Röblitz S., Wunder D., Saleh L., Schippert C., Hengartner M.P. Cognitive function in association with high estradiol levels resulting from fertility treatment Hormones and Behavior 10.1016/j.yhbeh.2021.104951 no Serum Elecsys NA
Shirazi T.N., Levenberg K., Cunningham H., Self H., Dawood K., Cárdenas R., Ortiz T.L., Carré J.M., Breedlove S.M., Puts D.A. Relationships between ovarian hormone concentrations and mental rotations performance in naturally-cycling women Hormones and Behavior 10.1016/j.yhbeh.2020.104886 yes Saliva Diagnostic Systems Laboratories, Webster NA
Schmalenberger K.M., Tauseef H.A., Barone J.C., Owens S.A., Lieberman L., Jarczok M.N., Girdler S.S., Kiesner J., Ditzen B., Eisenlohr-Moul T.A. How to study the menstrual cycle: Practical tools and recommendations Psychoneuroendocrinology 10.1016/j.psyneuen.2020.104895 not empirical NA NA NA
Wang J., Chen A. High progesterone levels facilitate women's social information processing by optimizing attention allocation Psychoneuroendocrinology 10.1016/j.psyneuen.2020.104882 yes Saliva Salimetrics NA
Bernal A., Mateo-Martínez R., Paolieri D. Influence of sex, menstrual cycle, and hormonal contraceptives on egocentric navigation with or without landmarks Psychoneuroendocrinology 10.1016/j.psyneuen.2020.104768 yes Saliva Labor Diagnostika Nord NA
Strahler J., Hermann A., Schmidt N.M., Stark R., Hennig J., Munk A.J. Food cue-elicited brain potentials change throughout menstrual cycle: Modulation by eating styles, negative affect, and premenstrual complaints Hormones and Behavior 10.1016/j.yhbeh.2020.104811 yes Saliva IBL NA
Klatt W.K., Mayer B., Lobmaier J.S. Content matters: Cyclic effects on women's voices depend on social context Hormones and Behavior 10.1016/j.yhbeh.2020.104762 yes Saliva LCMS/MS Kirschbaum NA
Luine V., Frankfurt M. Estrogenic regulation of memory: The first 50 years Hormones and Behavior 10.1016/j.yhbeh.2020.104711 not empirical NA NA NA
Peragine D., Simeon-Spezzaferro C., Brown A., Gervais N.J., Hampson E., Einstein G. Sex difference or hormonal difference in mental rotation? The influence of ovarian milieu Psychoneuroendocrinology 10.1016/j.psyneuen.2019.104488 yes app-based NA focus on hormone therapy x cycle phase
Hahn A.C., DeBruine L.M., Pesce L.A., Diaz A., Aberson C.L., Jones B.C. Does women's anxious jealousy track changes in steroid hormone levels? Psychoneuroendocrinology 10.1016/j.psyneuen.2019.104553 yes Saliva Salimetrics NA
Hampson E. A brief guide to the menstrual cycle and oral contraceptive use for researchers in behavioral endocrinology Hormones and Behavior 10.1016/j.yhbeh.2019.104655 not empirical NA NA NA
Li S.H., Lloyd A.R., Graham B.M. Physical and mental fatigue across the menstrual cycle in women with and without generalised anxiety disorder Hormones and Behavior 10.1016/j.yhbeh.2019.104667 yes Saliva Salimetrics NA
Ney L.J., Gogos A., Ken Hsu C.-M., Felmingham K.L. An alternative theory for hormone effects on sex differences in PTSD: The role of heightened sex hormones during trauma Psychoneuroendocrinology 10.1016/j.psyneuen.2019.104416 not empirical NA NA NA
Egan A.E., Seemiller L.R., Packard A.E.B., Solomon M.B., Ulrich-Lai Y.M. Palatable food reduces anxiety-like behaviors and HPA axis responses to stress in female rats in an estrous-cycle specific manner Hormones and Behavior 10.1016/j.yhbeh.2019.07.005 not humans NA NA NA
Catena T.M., Simmons Z.L., Roney J.R. Do women's faces become more attractive near ovulation? Hormones and Behavior 10.1016/j.yhbeh.2019.07.008 yes Saliva Salimetrics NA
Ji T., Tybur J.M., Kandrik M., Faure R., van Vugt M. Women's implicit bias against threatening male faces: The role of emotion, hormones, and group membership Hormones and Behavior 10.1016/j.yhbeh.2019.06.012 yes Saliva immunoassay NA
Lobmaier J.S., Probst F., Lory V., Meyer A.H., Meinlschmidt G. Increased sensitivity to social exclusion during the luteal phase: Progesterone as resilience factor buffering against ostracism? Psychoneuroendocrinology 10.1016/j.psyneuen.2019.05.019 yes Saliva IBL NA
Pilarczyk J., Schwertner E., Wołoszyn K., Kuniecki M. Phase of the menstrual cycle affects engagement of attention with emotional images Psychoneuroendocrinology 10.1016/j.psyneuen.2019.02.009 yes Saliva DRG prog only
Mulligan E.M., Hajcak G., Klawohn J., Nelson B., Meyer A. Effects of menstrual cycle phase on associations between the error-related negativity and checking symptoms in women Psychoneuroendocrinology 10.1016/j.psyneuen.2019.01.027 yes Saliva Salimetrics NA
Raymond C., Marin's M.-F., Juster R.-P., Leclaire S., Bourdon O., Cayer-Falardeau S., Lupien S.J. Increased frequency of mind wandering in healthy women using oral contraceptives Psychoneuroendocrinology 10.1016/j.psyneuen.2018.11.005 yes Saliva Salimetrics NA
White E.C., Graham B.M. Low estradiol is linked to increased skin conductance, but not subjective anxiety or affect, in response to an impromptu speech task Psychoneuroendocrinology 10.1016/j.psyneuen.2018.07.023 yes Serum Siemens Immunoassay NA
Jones B.C., Hahn A.C., Fisher C.I., Wang H., Kandrik M., Lao J., Han C., Lee A.J., Holzleitner I.J., DeBruine L.M. No compelling evidence that more physically attractive young adult women have higher estradiol or progesterone Psychoneuroendocrinology 10.1016/j.psyneuen.2018.07.026 yes Saliva Salimetrics NA
Bachofner L.M., Lobmaier J.S. Towards a more holistic view of fertility: The need to consider biological underpinnings rather than only data Hormones and Behavior 10.1016/j.yhbeh.2018.07.004 not empirical NA NA NA
Lobmaier J.S., Bachofner L.M. Timing is crucial: Some critical thoughts on using LH tests to determine women's current fertility Hormones and Behavior 10.1016/j.yhbeh.2018.07.005 not empirical NA NA NA
Jünger J., Motta-Mena N.V., Cardenas R., Bailey D., Rosenfield K.A., Schild C., Penke L., Puts D.A. Do women's preferences for masculine voices shift across the ovulatory cycle? Hormones and Behavior 10.1016/j.yhbeh.2018.10.008 yes Saliva IBL NA
Roney J.R. Hormonal mechanisms and the optimal use of luteinizing hormone tests in human menstrual cycle research Hormones and Behavior 10.1016/j.yhbeh.2018.05.021 not empirical NA NA NA
Gurvich C., Gavrilidis E., Worsley R., Hadaib A., Thomas N., Kulkarni J. Menstrual cycle irregularity and menopause status influence cognition in women with schizophrenia Psychoneuroendocrinology 10.1016/j.psyneuen.2018.06.022 sort of (between-subjects study on irregularity) Serum unclear NA
Geniole S.N., Carré J.M. Human social neuroendocrinology: Review of the rapid effects of testosterone Hormones and Behavior 10.1016/j.yhbeh.2018.06.001 not empirical NA NA NA
Shirazi T.N., Bossio J.A., Puts D.A., Chivers M.L. Menstrual cycle phase predicts women's hormonal responses to sexual stimuli Hormones and Behavior 10.1016/j.yhbeh.2018.05.023 yes Saliva Salimetrics NA
Dixson B.J.W., Blake K.R., Denson T.F., Gooda-Vossos A., O'Dean S.M., Sulikowski D., Rantala M.J., Brooks R.C. The role of mating context and fecundability in women's preferences for men's facial masculinity and beardedness Psychoneuroendocrinology 10.1016/j.psyneuen.2018.04.007 yes Saliva IBL NA
Marcinkowska U.M., Kaminski G., Little A.C., Jasienska G. Average ovarian hormone levels, rather than daily values and their fluctuations, are related to facial preferences among women Hormones and Behavior 10.1016/j.yhbeh.2018.05.013 yes Saliva DRG NA
Reynolds T.A., Makhanova A., Marcinkowska U.M., Jasienska G., McNulty J.K., Eckel L.A., Nikonova L., Maner J.K. Progesterone and women's anxiety across the menstrual cycle Hormones and Behavior 10.1016/j.yhbeh.2018.04.008 yes Saliva DRG NA
Munk A.J.L., Zoeller A.C., Hennig J. Fluctuations of estradiol during women's menstrual cycle: Influences on reactivity towards erotic stimuli in the late positive potential Psychoneuroendocrinology 10.1016/j.psyneuen.2018.02.028 yes Saliva IBL NA
Griksiene R., Monciunskaite R., Arnatkeviciute A., Ruksenas O. Does the use of hormonal contraceptives affect the mental rotation performance? Hormones and Behavior 10.1016/j.yhbeh.2018.03.004 yes Saliva IBL NA
Hornung J., Noack H., Thomas M., Farger G., Nieratschker V., Freiherr J., Derntl B. Bayesian informed evidence against modulation of androstadienone-effects by genotypic receptor variants and participant sex: A study assessing Stroop interference control, mood and olfaction Hormones and Behavior 10.1016/j.yhbeh.2017.12.003 yes Saliva unclear (lab: SwisshealthMed) NA
Jones B.C., Hahn A.C., Fisher C.I., Wang H., Kandrik M., DeBruine L.M. General sexual desire, but not desire for uncommitted sexual relationships, tracks changes in women's hormonal status Psychoneuroendocrinology 10.1016/j.psyneuen.2017.12.015 yes Saliva Salimetrics NA
Roberts B., Eisenlohr-Moul T., Martel M.M. Reproductive steroids and ADHD symptoms across the menstrual cycle Psychoneuroendocrinology 10.1016/j.psyneuen.2017.11.015 yes Saliva Salimetrics NA
Marcinkowska U.M., Galbarczyk A., Jasienska G. La donna è mobile? Lack of cyclical shifts in facial symmetry, and facial and body masculinity preferences—A hormone based study Psychoneuroendocrinology 10.1016/j.psyneuen.2017.11.007 yes Saliva DRG NA
Dixson B.J.W., Lee A.J., Blake K.R., Jasienska G., Marcinkowska U.M. Women's preferences for men's beards show no relation to their ovarian cycle phase and sex hormone levels Hormones and Behavior 10.1016/j.yhbeh.2017.11.006 yes Saliva DRG NA
Armbruster D., Kirschbaum C., Strobel A. The not-so-bitter pill: Effects of combined oral contraceptives on peripheral physiological indicators of emotional reactivity Hormones and Behavior 10.1016/j.yhbeh.2017.06.009 yes Saliva IBL NA
Merz C.J. Contribution of stress and sex hormones to memory encoding Psychoneuroendocrinology 10.1016/j.psyneuen.2017.05.002 yes Saliva Demeditec Diagnostics NA
Ditzen B., Palm-Fischbacher S., Gossweiler L., Stucky L., Ehlert U. Effects of stress on women's preference for male facial masculinity and their endocrine correlates Psychoneuroendocrinology 10.1016/j.psyneuen.2017.05.006 yes Saliva IBL NA
Aleknaviciute J., Tulen J.H.M., De Rijke Y.B., Bouwkamp C.G., van der Kroeg M., Timmermans M., Wester V.L., Bergink V., Hoogendijk W.J.G., Tiemeier H., van Rossum E.F.C., Kooiman C.G., Kushner S.A. The levonorgestrel-releasing intrauterine device potentiates stress reactivity Psychoneuroendocrinology 10.1016/j.psyneuen.2017.02.025 no NA NA NA
Graham B.M., Ash C., Den M.L. High endogenous estradiol is associated with enhanced cognitive emotion regulation of physiological conditioned fear responses in women Psychoneuroendocrinology 10.1016/j.psyneuen.2017.02.023 yes Serum Siemens Immunoassay between-subjects, median split E2
Stanton S.J. The role of testosterone and estrogen in consumer behavior and social & economic decision making: A review Hormones and Behavior 10.1016/j.yhbeh.2016.11.006 not empirical NA NA NA
Motta-Mena N.V., Puts D.A. Endocrinology of human female sexuality, mating, and reproductive behavior Hormones and Behavior 10.1016/j.yhbeh.2016.11.012 not empirical NA NA NA
Roney J.R., Simmons Z.L. Ovarian hormone fluctuations predict within-cycle shifts in women's food intake Hormones and Behavior 10.1016/j.yhbeh.2017.01.009 yes Saliva Salimetrics NA
Ozgocer T., Ucar C., Yildiz S. Cortisol awakening response is blunted and pain perception is increased during menses in cyclic women Psychoneuroendocrinology 10.1016/j.psyneuen.2016.12.011 yes Saliva DRG NA
Hamstra D.A., de Kloet E.R., Quataert I., Jansen M., Van der Does W. Mineralocorticoid receptor haplotype, estradiol, progesterone and emotional information processing Psychoneuroendocrinology 10.1016/j.psyneuen.2016.11.037 yes Saliva IBL NA
Lundin C., Danielsson K.G., Bixo M., Moby L., Bengtsdotter H., Jawad I., Marions L., Brynhildsen J., Malmborg A., Lindh I., Sundström Poromaa I. Combined oral contraceptive use is associated with both improvement and worsening of mood in the different phases of the treatment cycle—A double-blind, placebo-controlled randomized trial Psychoneuroendocrinology 10.1016/j.psyneuen.2016.11.033 no NA NA NA
Blake K.R., Bastian B., O'Dean S.M., Denson T.F. High estradiol and low progesterone are associated with high assertiveness in women Psychoneuroendocrinology 10.1016/j.psyneuen.2016.10.008 yes Saliva IBL NA

Small re-analysis of urinary validity data

Roos et al. 2015 and Johnson et al. 2015 report aggregated data for E3g and Pdg in urine, as well as E2 and P4 in serum.

Show code 
ser <- rio::import("data/tabula-roos2015.tsv")
uri <- rio::import("data/tabula-2015_Urinary_hormones_nomograms_Gnoth_Clin_Chem_Lab_Med.tsv")
seruri <- ser %>% left_join(uri, by = c("Day relative to ovulation"), suffix = c("_serum", "_urine")) %>% mutate_all(~ as.numeric(str_match(., "([0-9.]+)( \\()?")[,2]))
cor.test(seruri$`E2 (pmol/l)`, seruri$`E3G, ng/mL`)


    Pearson's product-moment correlation

data:  seruri$`E2 (pmol/l)` and seruri$`E3G, ng/mL`
t = 6.2797, df = 26, p-value = 1.201e-06
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.5676318 0.8912575
sample estimates:
      cor 
0.7763094
Show code 
cor.test(seruri$`E2 (pmol/l)`, lead(seruri$`E3G, ng/mL`))


    Pearson's product-moment correlation

data:  seruri$`E2 (pmol/l)` and lead(seruri$`E3G, ng/mL`)
t = 10.164, df = 25, p-value = 2.309e-10
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.7849329 0.9525164
sample estimates:
      cor 
0.8972964
Show code 
psych::cor.wt(cbind(seruri$`E2 (pmol/l)`, lead(seruri$`E3G, ng/mL`)), w = seruri$n_serum*seruri$n_urine)

Weighted Correlations 
Call:psych::cor.wt(data = cbind(seruri$`E2 (pmol/l)`, lead(seruri$`E3G, ng/mL`)), 
    w = seruri$n_serum * seruri$n_urine)
   C1   C2  
R1 1.00     
R2 0.96 1.00
Show code 
cor.test(seruri$`Progesterone (nmol/l)`, seruri$`P3G, ug/mL`)


    Pearson's product-moment correlation

data:  seruri$`Progesterone (nmol/l)` and seruri$`P3G, ug/mL`
t = 19.052, df = 26, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9270102 0.9843314
sample estimates:
      cor 
0.9659998
Show code 
cor.test(seruri$`Progesterone (nmol/l)`, lead(seruri$`P3G, ug/mL`))


    Pearson's product-moment correlation

data:  seruri$`Progesterone (nmol/l)` and lead(seruri$`P3G, ug/mL`)
t = 25.224, df = 25, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 0.9580096 0.9913804
sample estimates:
      cor 
0.9809147
Show code 
psych::cor.wt(cbind(seruri$`Progesterone (nmol/l)`, lead(seruri$`P3G, ug/mL`)), w = seruri$n_serum*seruri$n_urine)

Weighted Correlations 
Call:psych::cor.wt(data = cbind(seruri$`Progesterone (nmol/l)`, lead(seruri$`P3G, ug/mL`)), 
    w = seruri$n_serum * seruri$n_urine)
   C1   C2  
R1 1.00     
R2 0.98 1.00