Aijun Ye

Age at Menarche and Metabolic Markers for Type 2 Diabetes in Premenopausal Women: The BioCycle Study

CONTEXT Early age at menarche has been linked to an elevated risk of type 2 diabetes. However, the underlying mechanism is unclear. OBJECTIVE Our objective was to examine associations between age at menarche and type 2 diabetes risk factors. DESIGN, PARTICIPANTS, AND SETTING The BioCycle Study followed 253 healthy premenopausal women from the general population (Buffalo, NY) for one to two menstrual cycles. MAIN OUTCOME MEASURES Age at menarche was self-reported. Body mass index and waist circumference were measured by trained personnel. Total body and trunk fat were measured by dual-energy x-ray absorptiometry. Fasting glucose, insulin, highly sensitive C-reactive protein, and SHBG levels were measured up to eight times per cycle. Insulin resistance (IR) and β-cell function were evaluated using the homeostasis model assessment (HOMA)-IR and HOMA-β. RESULTS The mean age at menarche was 12.5 ± 1.2 yr. After adjustment for age, race, education, and physical activity, early menarche (≤12 yr) was significantly associated with an increase of 1.35 kg/m(2) in body mass index (P = 0.01), 2.52% in percent total body fat (P = 0.004), 3.02% in percent trunk fat (P = 0.004), 0.15 μIU/ml in (log)insulin (P = 0.02), 0.15 U in (log)HOMA-IR (P = 0.03), and 0.16 U in (log)HOMA-β (P = 0.01) compared with average menarche (12-14 yr). No associations were found for SHBG or highly sensitive C-reactive protein. CONCLUSIONS Early onset of menarche is associated with unfavorable metabolic phenotypes compared with average onset of menarche in healthy premenopausal women, including reduced insulin sensitivity and β-cell function and greater total and trunk fat.

Longitudinal Study of Insulin Resistance and Sex Hormones over the Menstrual Cycle: The BioCycle Study

CONTEXT Conflicting findings have been reported regarding the effect of menstrual cycle phase and sex hormones on insulin sensitivity. OBJECTIVE The aim was to determine the pattern of insulin resistance over the menstrual cycle and whether variations in sex hormones explain these patterns. DESIGN The BioCycle study is a longitudinal study that measured hormones at different phases of the menstrual cycle. Participants had up to eight visits per cycle; each visit was timed using fertility monitors to capture sensitive windows of hormonal changes. SETTING The study was conducted in the general community of the University at Buffalo (Buffalo, NY). PARTICIPANTS A total of 257 healthy, premenopausal women (age, 27±8 yr; body mass index, 24±4 kg/m2) participated in the study. MAIN OUTCOME MEASURES We measured fasting insulin, glucose, and insulin resistance by the homeostasis model of insulin resistance (HOMA-IR). RESULTS Significant changes in HOMA-IR were observed over the menstrual cycle; from a midfollicular phase level of 1.35, levels rose to 1.59 during the early luteal phase and decreased to 1.55 in the late-luteal phase. HOMA-IR levels primarily reflected changes in insulin and not glucose. After adjustment for age, race, cycle, and other sex hormones, HOMA-IR was positively associated with estradiol (β=0.082; P<0.001) and progesterone (β=0.025; P<0.001), and inversely associated with FSH (adjusted β=-0.040; P<0.001) and SHBG (β=-0.085; P<0.001). LH was not associated with HOMA-IR. Further adjustment for BMI weakened the association with SHBG (β=-0.057; P=0.06) but did not affect other associations. CONCLUSION Insulin exhibited minor menstrual cycle variability. Estradiol and progesterone were positively associated with insulin resistance and should be considered in studies of insulin resistance among premenopausal women.

Associations between urinary phthalate concentrations and semen quality parameters in a general population

STUDY QUESTION Are urinary phthalate concentrations associated with altered semen quality parameters among males recruited from the general population? SUMMARY ANSWER Urinary levels of metabolites of phthalate diesters are associated with lower total sperm counts, larger sperm head sizes, and higher percentages of morphologically abnormal sperm. WHAT IS KNOWN ALREADY High dose experiments in rats implicate phthalates as anti-androgens. Studies involving infertile men seeking care suggest that phthalates influence measures of semen quality raising concern about the implications for men in the general population. STUDY DESIGN, SIZE, DURATION This prospective cohort study comprised 501 male partners in couples discontinuing contraception to become pregnant, who were recruited from 16 US counties using population-based sampling frameworks from 2005 to 2009. PARTICIPANTS/MATERIALS, SETTING, METHODS Urine and semen samples were obtained at baseline from 473 (94%) men, of whom 378 (80%) men provided a second sample the following month. Urine was analyzed for 14 monoester metabolites of phthalate diesters by high-performance liquid chromatography coupled to tandem mass spectrometry. Semen samples were analyzed for 34 quality parameters categorized as general, motility, morphology, sperm head and sperm chromatin structure. MAIN RESULTS AND THE ROLE OF CHANCE Urinary mono-[2-(carboxymethyl) hexyl] phthalate (MCMHP), mono-(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono-benzyl phthalate (MBzP), and mono-isononyl phthalate (MNP) were significantly associated with lower total sperm counts and concentrations, larger sperm head sizes, higher proportions of megalo head sperm morphology, and/or other morphological changes. Urinary mono-methyl phthalate (MMP) and mono-cyclohexyl phthalate (MCPP) were significantly associated with lower sperm motility, and urine mono-2-ethylhexyl phthalate (MEHP) was significantly associated with higher sperm motility. LIMITATIONS, REASONS FOR CAUTION While adverse associations were observed, the implications of the findings for couple fecundity and fertility remain to be established. Cautious interpretation is needed in light of reliance on a single measurement of phthalate measure and no correction for multiple comparisons.

Accuracy Loss Due to Selection Bias in Cohort Studies with Left Truncation

BACKGROUND Selection is a common problem in paediatric and perinatal epidemiology, and truncation can be thought of as missing person time that can result in selection bias. Left truncation, also known as late or staggered entry, may induce selection bias and/or adversely affect precision. There are two kinds of left truncation: fixed left truncation where the start of follow-up is initiated at a set time, and variable left truncation where follow-up begins at a stochastically varying time-point. METHODS Using data from a time-to-pregnancy study, augmented by a simulation study, we demonstrate the effects of fixed and variable truncation on estimates of the hazard ratio. RESULTS First, fixed or variable non-differential left truncation results in a loss of precision. Fixed or variable differential left truncation results in a bias either towards or away from the null as well as a loss of precision. The extent and direction of this bias is a function of the size and direction of the association between exposure and outcome, and occurs in common scenarios and under a wide range of conditions. CONCLUSIONS As demonstrated in simulation studies, selection bias due to left truncation could have a serious impact on inferences, especially in the case of fixed or variable differential left truncation. When present in epidemiologic studies, proper accounting for left truncation is just as important as proper accounting for right censoring.

Caffeinated beverage intake and reproductive hormones among premenopausal women in the BioCycle Study.

BACKGROUND Caffeinated beverages are widely consumed among women of reproductive age, but their association with reproductive hormones, and whether race modifies any such associations, is not well understood. OBJECTIVE We assessed the relation between caffeine and caffeinated beverage intake and reproductive hormones in healthy premenopausal women and evaluated the potential effect modification by race. DESIGN Participants (n = 259) were followed for up to 2 menstrual cycles and provided fasting blood specimens for hormonal assessment at up to 8 visits per cycle and four 24-h dietary recalls per cycle. Weighted linear mixed models and nonlinear mixed models with harmonic terms were used to estimate associations between caffeine and hormone concentrations, adjusted for age, adiposity, physical activity, energy and alcohol intakes, and perceived stress. On the basis of a priori assumptions, an interaction between race and caffeine was tested, and stratified results are presented. RESULTS Caffeine intake ≥200 mg/d was inversely associated with free estradiol concentrations among white women (β = -0.15; 95% CI: -0.26, -0.05) and positively associated among Asian women (β = 0.61; 95% CI: 0.31, 0.92). Caffeinated soda intake and green tea intake ≥1 cup/d (1 cup = 240 mL) were positively associated with free estradiol concentrations among all races: β = 0.14 (95% CI: 0.06, 0.22) and β = 0.26 (95% CI: 0.07, 0.45), respectively. CONCLUSIONS Moderate consumption of caffeine was associated with reduced estradiol concentrations among white women, whereas caffeinated soda and green tea intakes were associated with increased estradiol concentrations among all races. Further research is warranted on the association between caffeine and caffeinated beverages and reproductive hormones and whether these relations differ by race.

The influence of sporadic anovulation on hormone levels in ovulatory cycles

STUDY QUESTION Do ovulatory hormone profiles among healthy premenopausal women differ between women with and without sporadic anovulation? SUMMARY ANSWER Women with one anovulatory cycle tended to have lower estradiol, progesterone and LH peak levels during their ovulatory cycle. WHAT IS KNOWN ALREADY Anovulation occurs sporadically in healthy premenopausal women, but the influence of hormones in a preceding cycle and the impact on a subsequent cycles hormone levels is unknown. STUDY DESIGN, SIZE, DURATION The BioCycle Study was a prospective cohort including 250 healthy regularly menstruating women, 18-44 years of age, from Western New York with no history of menstrual or ovulation disorders. The women were followed with up to eight study visits per cycle for two cycles, most of which were consecutive. PARTICIPANTS/MATERIALS, SETTING AND METHODS All study visits were timed to menstrual cycle phase using fertility monitors and located at the University at Buffalo womens health research center from 2005 to 2007. The main outcomes measured were estradiol, progesterone, LH and follicle-stimulating hormone levels in serum at up to 16 visits over two cycles. Anovulation was defined as peak serum progesterone concentrations ≤5 ng/ml and no serum LH peak detected during the mid- or late-luteal phase visit. MAIN RESULTS AND THE ROLE OF CHANCE Reproductive hormone concentrations were lower during anovulatory cycles, but significant reductions were also observed in estradiol (-25%, P = 0.003) and progesterone (-22%, P = 0.001) during the ovulatory cycles of women with one anovulatory cycle compared with women with two ovulatory cycles. LH peak concentrations were decreased in the ovulatory cycle of women with an anovulatory cycle (significant amplitude effect, P = 0.004; geometric mean levels 38% lower, P < 0.05). LIMITATIONS, REASONS FOR CAUTION Follow-up was limited to two menstrual cycles, and no ultrasound assessment of ovulation was available. Data were missing for a total of 168 of a possible 4072 cycle visits (4.1%), though all women had at least five visits per cycle (94% had seven or more per cycle). WIDER IMPLICATIONS OF THE FINDINGS These results suggest a possible underlying cause of anovulation, such as a longer-term subclinical follicular, ovarian or hypothalamic/pituitary dysfunction, even among healthy, regularly menstruating women.

Adiposity and sex hormones across the menstrual cycle: the BioCycle Study.

Objective:To investigate the influence of adiposity on patterns of sex hormones across the menstrual cycle among regularly menstruating women.Subjects:The BioCycle Study followed 239 healthy women for 1–2 menstrual cycles, with up to eight visits per cycle timed using fertility monitors.Methods:Serum estradiol (E2), progesterone, luteinizing hormone (LH), follicle-stimulating hormone (FSH) and sex hormone-binding globulin (SHBG) were measured at each visit. Adiposity was measured by anthropometry and by dual energy X-ray absorptiometry (DXA). Differences in hormonal patterns by adiposity measures were estimated using nonlinear mixed models, which allow for comparisons in overall mean levels, amplitude (i.e., lowest to highest level within each cycle) and shifts in timing of peaks while adjusting for age, race, energy intake and physical activity.Results:Compared with normal weight women (n=154), obese women (body mass index (BMI) ⩾30 kg m−2, n=25) averaged lower levels of progesterone (−15%, P=0.003), LH (−17%, P=0.01), FSH (−23%, P=0.001) and higher free E2 (+22%, P=0.0001) across the cycle. To lesser magnitudes, overweight women (BMI: 25–30, n=60) also exhibited differences in the same directions for mean levels of free E2, FSH and LH. Obese women experienced greater changes in amplitude of LH (9%, P=0.002) and FSH (8%, P=0.004), but no differences were observed among overweight women. Higher central adiposity by top compared to bottom tertile of trunk-to-leg fat ratio by DXA was associated with lower total E2 (−14%, P=0.005), and FSH (−15%, P=0.001). Peaks in FSH and LH occurred later (∼0.5 day) in the cycle among women with greater central adiposity.Conclusion:Greater total and central adiposity were associated with changes in mean hormone levels. The greater amplitudes observed among obese women suggest compensatory mechanisms at work to maintain hormonal homeostasis. Central adiposity may be more important in influencing timing of hormonal peaks than total adiposity.

Realignment and multiple imputation of longitudinal data: an application to menstrual cycle data

Reproductive hormone levels are highly variable among premenopausal women during the menstrual cycle. Accurate timing of hormone measurement is essential, especially when investigating day- or phase-specific effects. The BioCycle Study used daily urine home fertility monitors to help detect the luteinising hormone (LH) surge in order to schedule visits with biologically relevant windows of hormonal variability. However, as the LH surge is brief and cycles vary in length, relevant hormonal changes may not align with scheduled visits even when fertility monitors are used. Using monitor data, measurements were reclassified according to biological phase of the menstrual cycle to more accurate cycle phase categories. Longitudinal multiple imputation methods were applied after reclassification if no visit occurred during a given menstrual cycle phase. Reclassified cycles had more clearly defined hormonal profiles, with higher mean peak hormones (up to 141%) and reduced variability (up to 71%). We demonstrate the importance of realigning visits to biologically relevant windows when assessing phase- or day-specific effects and the feasibility of applying longitudinal multiple imputation methods. Our method has applications in settings where missing data may occur over time, where daily blood sampling for hormonal measurements is not feasible, and in other areas where timing is essential.

Correlated Biomarker Measurement Error: An Important Threat to Inference in Environmental Epidemiology

Utilizing multiple biomarkers is increasingly common in epidemiology. However, the combined impact of correlated exposure measurement error, unmeasured confounding, interaction, and limits of detection (LODs) on inference for multiple biomarkers is unknown. We conducted data-driven simulations evaluating bias from correlated measurement error with varying reliability coefficients (R), odds ratios (ORs), levels of correlation between exposures and error, LODs, and interactions. Blood cadmium and lead levels in relation to anovulation served as the motivating example, based on findings from the BioCycle Study (2005-2007). For most scenarios, main-effect estimates for cadmium and lead with increasing levels of positively correlated measurement error created increasing downward or upward bias for OR > 1.00 and OR < 1.00, respectively, that was also a function of effect size. Some scenarios showed bias for cadmium away from the null. Results subject to LODs were similar. Bias for main and interaction effects ranged from -130% to 36% and from -144% to 84%, respectively. A closed-form continuous outcome case solution provides a useful tool for estimating the bias in logistic regression. Investigators should consider how measurement error and LODs may bias findings when examining biomarkers measured in the same medium, prepared with the same process, or analyzed using the same method.

Validation of different instruments for caffeine measurement among premenopausal women in the BioCycle study.

Effects of caffeine on womens health are inconclusive, in part because of inadequate exposure assessment. In this study we determined 1) validity of a food frequency questionnaire compared with multiple 24-hour dietary recalls (24HDRs) for measuring monthly caffeine and caffeinated beverage intakes; and 2) validity of the 24HDR compared with the prior days diary record for measuring daily caffeinated coffee intake. BioCycle Study (2005-2007) participants, women (n = 259) aged 18-44 years from western New York State, were followed for 2 menstrual cycles. Participants completed a food frequency questionnaire at the end of each cycle, four 24HDRs per cycle, and daily diaries. Caffeine intakes reported for the food frequency questionnaires were greater than those reported for the 24HDRs (mean = 114.1 vs. 92.6mg/day, P = 0.01) but showed high correlation (r = 0.73, P < 0.001) and moderate agreement (К = 0.51, 95% confidence interval: 0.43, 0.57). Women reported less caffeinated coffee intake in their 24HDRs compared with their corresponding diary days (mean = 0.51 vs. 0.80 cups/day, P < 0.001) (1 cup = 237 mL). Although caffeine and coffee exposures were highly correlated, absolute intakes differed significantly between measurement tools. These results highlight the importance of considering potential misclassification of caffeine exposure.