Where in the world? Latitude, longitude and season contribute to the complex co-ordinates determining cortisol levels.

OBJECTIVE: Cortisol is a critical stress hormone with circadian rhythms synchronized by light. There are seasonal differences in expression of pro-inflammatory genes and in some diseases moderated by glucocorticoids. As light changes with season and with latitude and longitude, we assessed changes in population cortisol associated with these parameters. DESIGN: Retrospective data audit. PATIENTS: Populations across 4 states of Australia over 3 years. MEASUREMENTS: Serum cortisol levels, age, gender, time of collection, sunrise time, season and location were determined. RESULTS: In 4 geographically separate populations (n = 84 937), sunrise time and time of sample collection were the most important factors influencing median cortisol. Over 2 hours in the morning cortisol could decrease by up to 76 nmol/L, and for each hour that sunrise time advanced there was up to 6.9% increase in cortisol. A cyclic seasonal pattern of cortisol was confirmed each year in all populations with autumn/winter cortisol highest compared to spring/summer with differences of up to 44 nmol/L. There was less change in cortisol in latitudes closer to the equator but cortisol progressively increased from 25 to 30°S of the equator. In more southerly latitudes, seasonal cortisol variation also increased, and over the entire latitude range, there was up to 50 nmol/L change in cortisol. Longitude variation within a time zone had a minimal effect on median cortisol. CONCLUSIONS: Location, time of year and time of day are important influences on population cortisol levels. Elevated autumn/winter morning cortisol levels are likely due to sampling closer to the circadian peak due to later sunrise time. Understanding how the environment can influence cortisol levels may further our knowledge of physiology and disease.

Immuno-nephelometric determination of group streptococcal anti-streptolysin O titres (ASOT) from dried blood spots: Method for validating a new assay.

This study was designed to determine the sensitivity and reproducibility of recovering anti-streptolysin O titres (ASOT) from dried blood spot (DBS) samples, a methodologic subcomponent of the penicillin pharmacokinetic studies in children receiving secondary prophylaxis with intramuscular benzathine penicillin for acute rheumatic fever.

Quantifying the intraindividual variation of antimüllerian hormone in the ovarian cycle.

OBJECTIVE: To quantify intraindividual variability of antimüllerian hormone (AMH) as analytical and biological coefficients of variation and assess the effects of variation on clinical classification. DESIGN: Retrospective cohort study. SETTING: Not applicable. PATIENT(S): Thirty-eight women referred by general practitioners. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): Total intraindividual variability (CVW), analytical (CVA) and biological variability (CVI) for each woman and for AMH ranges: low (<5 pmol/L), reduced (5-10), moderate (>10-30) and high (>30 pmol/L), with calculation of proportion of women crossing clinical cutoffs and expected variability around each cutoff. RESULT(S): Cycling women (n = 38) contributed 238 blood samples (average 6 samples each). The average total intraindividual AMH variability was 20% (range: 2.1% to 73%). Biological variation was 19% (range: 0 to 71%) and at least twice the analytical variation of 6.9% (range: 4.5% to 16%). Reclassification rates were highest in women with low (33%) or reduced AMH (67%) levels. Expected variations around the 5, 10, and 30 pmol/L cutoffs were 3-7, 7-13, and 20-40 pmol/L, respectively. In a woman with mean AMH in the 10-30 pmol/L range, the span of results that could occur was 7-40 pmol/L. CONCLUSION(S): Total variation in AMH was 20%, and the majority of this was biological. Changes in AMH resulted in reclassification in 29% of women and occurred most frequently in those with low and reduced AMH. In cycling women, the variability in AMH should be considered by clinicians, especially if a result is close to a clinical cutoff.

Reconciling the Log-Linear and Non-Log-Linear Nature of the TSH-Free T4 Relationship: Intra-Individual Analysis of a Large Population.

CONTEXT: The TSH-T4 relationship was thought to be inverse log-linear, but recent cross-sectional studies report a complex, nonlinear relationship; large, intra-individual studies are lacking. OBJECTIVE: Our objective was to analyze the TSH-free T4 relationship within individuals. METHODS: We analyzed data from 13 379 patients, each with six or more TSH/free T4 measurements and at least a 5-fold difference between individual median TSH and minimum or maximum TSH. Linear and nonlinear regression models of log TSH on free T4 were fitted to data from individuals and goodness of fit compared by likelihood ratio testing. RESULTS: Comparing all models, the linear model achieved best fit in 31% of individuals, followed by quartic (27%), cubic (15%), null (12%), and quadratic (11%) models. After eliminating least favored models (with individuals reassigned to best fitting, available models), the linear model fit best in 42% of participants, quartic in 43%, and null model in 15%. As the number of observations per individual increased, so did the proportion of individuals in whom the linear model achieved best fit, to 66% in those with more than 20 observations. When linear models were applied to all individuals and averaged according to individual median free T4 values, variations in slope and intercept indicated a nonlinear log TSH-free T4 relationship across the population. CONCLUSIONS: The log TSH-free T4 relationship appears linear in some individuals and nonlinear in others, but is predominantly linear in those with the largest number of observations. A log-linear relationship within individuals can be reconciled with a non-log-linear relationship in a population.

Brief report: Does PTH increase with age, independent of 25-hydroxyvitamin D, phosphate, renal function, and ionized calcium?

CONTEXT: Circulating PTH concentrations increase with age. It is uncertain whether an age-related PTH increase occurs independent of changes in circulating 25-hydroxyvitamin D, phosphate, renal function, and ionized calcium. OBJECTIVE: The purpose of this article was to analyze the relationship between PTH and age, controlling for 25-hydroxyvitamin D, phosphate, renal function, and ionized calcium. METHODS: This was a retrospective, cross-sectional study analyzing the relationship between PTH and age in 2 independent datasets (laboratory 1, n = 17 275 and laboratory 2, n = 4878). We further analyzed subgroups after excluding participants with estimated glomerular filtration rate of <60 mL/min/1.73 m(2) or 25-hydroxyvitamin D of <50 nmol/L (for subgroups, n = 12 051 for laboratory 1 and 3473 for laboratory 2). RESULTS: After adjustment for sex, ionized calcium, 25-hydroxyvitamin D, phosphate, and estimated glomerular filtration rate, each 10-year increase in age was associated with a 5.0% increase in PTH (95% confidence interval [CI], 4.4%-5.6%; P < .001) in laboratory 1 and a 4.2% increase in laboratory 2 (95% CI, 3.0%-5.4%; P < .001). In the subgroups, each 10-year increase in age was associated with a 6.1% increase in PTH (95% CI, 5.5%-6.8%; P < .001) in laboratory 1 and a 4.9% increase (95% CI 3.5%-6.2%; P < .001) in laboratory 2. CONCLUSION: PTH concentrations increase with age, independent of 25-hydroxyvitamin D, ionized calcium, phosphate, and renal function. Further research is required to explore the underlying mechanisms and clinical relevance and to determine whether the use of age-related PTH reference ranges improves diagnostic accuracy, particularly in elderly individuals.

The effects of season, daylight saving and time of sunrise on serum cortisol in a large population.

Cortisol is critical for maintenance of health and homeostasis and factors affecting cortisol levels are of clinical importance. There is conflicting information about the effects of season on morning cortisol and little information on the effects of sunlight on population cortisol assessment. The aim of this study was to assess whether changes in median serum cortisol occurred in a population in conjunction with changing seasons, daylight saving time (DST) or time of sunrise. We analysed serum cortisol results (n = 27,569) from a single large laboratory over a 13-year period. Subjects with confounding medications or medical conditions were excluded and data analysed in 15-minute intervals. We assessed the influence of traditional seasons, seasons determined by equinox/solstice, DST and time of sunrise on median cortisol. The median time of cortisol collection did not vary significantly between seasons. Using traditional seasons, median cortisol was lowest in summer (386 nmol/L) and spring (384 nmol/L) with higher cortisol in autumn (406 nmol/L) and winter (414 nmol/L). Median cortisol was lowest in the summer solstice quarter with significant comparative increases in the spring equinox quarter (3.1%), the autumn equinox quarter (4.5%) and the winter solstice quarter (8.6%). When cortisol was modelled against time, with adjustment for actual sunrise time on day of collection, for each hour delay in sunrise there was a 4.8% increase in median cortisol (95% CI: 3.9-5.7%). In modelling to explain the variation in cortisol over the morning, sunrise time was better than season in explaining seasonal effects. A subtle cyclic pattern in median cortisol also occurred throughout the months of the year. A 3-year trial of DST allowed comparison of cortisol in DST and non DST periods, when clock time differed by one hour. There was modest evidence of a difference in acrophase between DST and non DST cortisol (p = 0.038), with DST peak cortisol estimated to occur 58 minutes later than non-DST peak. In summary, we found that time of sunrise and time of cortisol collection were the most important factors influencing median cortisol. For each hour later that the sun rose there was an almost 5% increase in median cortisol. There was significant seasonal variability with lowest cortisol noted in summer coinciding with the earliest sunrise time. This is an important finding which is consistent with the understanding that light is the major zeitgeber in entrainment of the human circadian cortisol rhythm. Our data suggest this rhythm is resistant to the arbitrary changes in clock time with daylight saving.

The relationship between TSH and free T4 in a large population is complex and nonlinear and differs by age and sex.

CONTEXT: The relationship between TSH and T4 is thought to be inverse log-linear, but recent studies have challenged this. There are limited data regarding age and sex differences in the TSH-T4 relationship. OBJECTIVE: The purpose of this study was to evaluate the TSH-free T4 relationship in a large sample. METHODS: In a cross-sectional, retrospective study, we analyzed TSH and free T4 results from 152 261 subjects collected over 12 years by a single laboratory. For each free T4 value (in picomoles per liter), the median TSH was calculated and analyzed by sex and age (in 20-year bands). RESULTS: The relationship between log TSH and free T4 was nonlinear. Mathematical modeling confirmed that it was described by 2 sigmoid curves with inflexion points at free T4 concentrations of 7 and 21 pmol/L. For free T4 within the reference range (10-20 pmol/L), median TSH was higher in men than in women (P < .001) and increased across age bands with the highest values in those 80 years and older (P < .001). In contrast, in overt hypothyroidism (n = 4403), TSH was lower in older age groups than in those aged 20-39 years (P < .001). CONCLUSIONS: The TSH-free T4 relationship is not inverse log-linear but can be described by 2 overlapping negative sigmoid curves. At physiological free T4 concentrations, TSH is higher in men and in older people, whereas the TSH response to hypothyroidism is more robust in younger people. These results advance understanding of the TSH-free T4 relationship, which is central to thyroid pathophysiology and laboratory diagnosis of thyroid disease.

Waiting for an elevated FSH--too late a marker of reduced ovarian reserve?

AIM: To assess age at which median follicle-stimulating hormone (FSH) is elevated above 10 U/L. BACKGROUND: Fertility and ovarian reserve decrease over the 4th decade with evidence that sensitive markers such as anti-Mullerian hormone fall even earlier. Despite its limitations, a basal or day 2-3 FSH is commonly used to assess ovarian reserve with levels over 10 U/L often used as a cut-point for further investigations. METHODS: Women referred to a community laboratory for 'hormone testing', including FSH and oestradiol (n = 40 254), were included in a retrospective analysis. Cases excluded were those with suppressed FSH (<1 U/L) who were likely on the oral contraceptive pill or pregnant and those with increased oestradiol (>500 pmol/L) who were likely approaching mid-cycle or pregnant. Remaining cases (n = 32 445) were analysed in five-year age bands for FSH median, mean, and 2.5 and 97.5 percentiles. RESULTS: Median FSH remained consistently low (≤5 U/L) in women ≤35 years of age and was 6 U/L in 35- to 40-year-olds. The mean FSH and 97.5 percentile increased steadily. The 97.5th percentile was 10 U/L or lower in women up to 30 years of age. CONCLUSIONS: Follicle-stimulating hormone is a late indicator of known reducing ovarian reserve, and in this study, median FSH did not increase over 10 U/L until >45 years of age. FSH levels >9 U/L were above the 97.5th percentile in those <25 years of age. If fertility is a concern, FSH levels persistently above age-specific medians in women under 40 years may prompt earlier follow-up with more sensitive tests for ovarian reserve.

Age-specific TSH reference ranges have minimal impact on the diagnosis of thyroid dysfunction.

OBJECTIVE: The use of age-specific reference ranges for TSH is advocated, but the impact of this on laboratory diagnosis of thyroid dysfunction is unclear. Our aims were to determine age-specific TSH reference ranges and to examine interassay differences in performance. DESIGN: We analysed TSH results from 223,045 consecutive samples assayed over 1 year by a single pathology provider using the Siemens Centaur assay. We excluded patients with evidence of thyroid disease to derive a reference population of 148,938 individuals and analysed results in the 5-year age bands. We reassayed 120 samples using three other methods (Architect, Roche and Immulite) to assess precision and bias. RESULTS: The 2·5th percentile for TSH was consistent across age groups (approximately 0·5 mU/l), whereas the 97·5th percentile increased from age 40 upwards, with the reference range upper limit being 3·75 mU/l at age 40 and 5·0 mU/l at age 90. In most age bands, the use of age-specific upper limits reclassified only 0·1-1·9% of participants as normal or abnormal compared with a common cut-off of 4·0 mU/l; in participants aged 85 years or more, reclassification rates were higher (2·1-4·7%). The four TSH assays showed good agreement at low-normal TSH concentrations (<2 mU/l), but at concentrations of 4·0 mU/l, there were intermethod differences of approximately 1 mU/l. CONCLUSION: The use of age-specific reference ranges for TSH has only minor effects on thyroid status, except in the very old. At high-normal TSH concentrations, between-method differences in performance have a comparable impact to that of age and may affect clinical decision-making.

Variation of female prolactin levels with menopausal status and phase of menstrual cycle.

BACKGROUND: Prolactin is not commonly recognised as a hormone that changes significantly within the menstrual cycle or after menopause. The aim of this study was to determine the degree of variability of prolactin in these physiological states. METHOD: Prolactin levels obtained from 6540 subjects between January 2006 and November 2008 were divided into five groups: men, postmenopausal women and premenopausal women in follicular/non-cycling, ovulatory and luteal phases. The median and 97.5th centile was determined for each group. The 97.5th centile was used to define the upper limit of prolactin. RESULTS: The prolactin median and upper limits were not significantly different in men and postmenopausal women. They were significantly higher in premenopausal women compared to men and postmenopausal women. Within premenopausal women, the prolactin median and upper limits were significantly higher in ovulatory phase compared to follicular/non-cycling and luteal phases and in luteal phase compared to follicular/non-cycling phase. CONCLUSIONS: Prolactin levels varied significantly throughout the menstrual cycle, and the utility and accuracy of prolactin testing may be improved by applying specific reference intervals for each phase of the menstrual cycle. Alternatively, a single reference interval could be used if prolactin is only measured in the follicular phase, well before midcycle. Prolactin levels in postmenopausal women and men were not significantly different, and a common prolactin reference interval may be appropriate. Further studies to confirm formal reference ranges for these groups may be clinically helpful.