Metabolic syndrome in pregnancy and risk for adverse pregnancy outcomes: A prospective cohort of nulliparous women

Background: Obesity increases the risk for developing gestational diabetes mellitus (GDM) and preeclampsia (PE), which both associate with increased risk for type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD) in women in later life. In the general population, metabolic syndrome (MetS) associates with T2DM and CVD. The impact of maternal MetS on pregnancy outcomes, in nulliparous pregnant women, has not been investigated. Methods and findings: Low-risk, nulliparous women were recruited to the multi-centre, international prospective Screening for Pregnancy Endpoints (SCOPE) cohort between 11 November 2004 and 28 February 2011. Women were assessed for a range of demographic, lifestyle, and metabolic health variables at 15 ± 1 weeks’ gestation. MetS was defined according to International Diabetes Federation (IDF) criteria for adults: waist circumference ≥80 cm, along with any 2 of the following: raised trigycerides (≥1.70 mmol/l [≥150 mg/dl]), reduced high-density lipoprotein cholesterol (<1.29 mmol/l [<50 mg/dl]), raised blood pressure (BP) (i.e., systolic BP ≥130 mm Hg or diastolic BP ≥85 mm Hg), or raised plasma glucose (≥5.6 mmol/l). Log-binomial regression analyses were used to examine the risk for each pregnancy outcome (GDM, PE, large for gestational age [LGA], small for gestational age [SGA], and spontaneous preterm birth [sPTB]) with each of the 5 individual components for MetS and as a composite measure (i.e., MetS, as defined by the IDF). The relative risks, adjusted for maternal BMI, age, study centre, ethnicity, socioeconomic index, physical activity, smoking status, depression status, and fetal sex, are reported. A total of 5,530 women were included, and 12.3% (n = 684) had MetS. Women with MetS were at an increased risk for PE by a factor of 1.63 (95% CI 1.23 to 2.15) and for GDM by 3.71 (95% CI 2.42 to 5.67). In absolute terms, for PE, women with MetS had an adjusted excess risk of 2.52% (95% CI 1.51% to 4.11%) and, for GDM, had an adjusted excess risk of 8.66% (95% CI 5.38% to 13.94%). Diagnosis of MetS was not associated with increased risk for LGA, SGA, or sPTB. Increasing BMI in combination with MetS increased the estimated probability for GDM and decreased the probability of an uncomplicated pregnancy. Limitations of this study are that there are several different definitions for MetS in the adult population, and as there are none for pregnancy, we cannot be sure that the IDF criteria are the most appropriate definition for pregnancy. Furthermore, MetS was assessed in the first trimester and may not reflect pre-pregnancy metabolic health status. Conclusions: We did not compare the impact of individual metabolic components with that of MetS as a composite, and therefore cannot conclude that MetS is better at identifying women at risk. However, more than half of the women who had MetS in early pregnancy developed a pregnancy complication compared with just over a third of women who did not have MetS. Furthermore, while increasing BMI increases the probability of GDM, the addition of MetS exacerbates this probability. Further studies are required to determine if individual MetS components act synergistically or independently

SOM clustering and modelling of Australian railway drivers’ sleep, wake, duty profiles

Two SOM ANN approaches were used in a study of Australian railway drivers (RDs) to classify RDs’ sleep/wake states and their sleep duration time series profiles over 14 days follow-up. The first approach was a feature-based SOM approach that clustered the most frequently occurring patterns of sleep. The second created RD networks of sleep/wake/duty/break feature parameter vectors of between-states transition probabilities via a multivariate extension of the mixture transition distribution (MTD) model, accommodating covariate interactions. SOM/ANN found 4 clusters of RDs whose sleep profiles differed significantly. Generalised Additive Models for Location, Scale and Shape of the 2 sleep outcomes confirmed that break and sleep onset times, break duration and hours to next duty are significant effects which operate differentially across the groups. Generally sleep increases for next duty onset between 10 am and 4 pm, and when hours since break onset exceeds 1 day. These 2 factors were significant factors determining current sleep, which have differential impacts across the clusters. Some drivers groups catch up sleep after the night shift, while others do so before the night shift. Sleep is governed by the RD’s anticipatory behaviour of next scheduled duty onset and hours since break onset, and driver experience, age and domestic scenario. This has clear health and safety implications for the rail industry. © Springer International Publishing Switzerland 2016

Insulin resistance in transgender individuals correlates with android fat mass

Background: Transgender individuals receiving gender-affirming hormone therapy (GAHT) are at increased risk of adverse cardiovascular outcomes. This may be related to effects on body composition and insulin resistance. Aims: To examine relationships between body fat distribution and insulin resistance in transgender individuals on established GAHT. Methods: Comparisons of body composition (dual energy X-ray absorptiometry) and insulin resistance [Homeostasis Model of Insulin Resistance (HOMA2-IR)] were made between transgender individuals (43 trans men and 41 trans women) on established GAHT (>12 months) and age-matched cisgender controls (30 males and 48 females). Multiple linear regressions were used to examine the relationship between HOMA2-IR and fat mass with gender, adjusting for age and total duration of GAHT and Pearson correlation coefficients are reported. Results: Compared with control cisgender women, trans men had mean difference of +7.8 kg (4.0, 11.5), p < 0.001 in lean mass and higher android:gynoid fat ratio [0.2 (0.1, 0.3), p < 0.001], but no difference in overall fat mass or insulin resistance. Compared with control cisgender men, trans women had median difference in lean mass of -6.9 kg (-10.6, -3.1), p < 0.001, fat mass of +9.8 kg (3.9, 14.5), p = 0.001, lower android:gynoid fat ratio -0.1 (-0.2,-0.0), p < 0.05), and higher insulin resistance 1.6 (1.3-1.9), p < 0.001). Higher HOMA2-IR correlated with higher android (r 2 = 0.712, p < 0.001) and gynoid (r 2 = 0.572, p < 0.001) fat mass in both trans men and trans women. Conclusion: Android fat more strongly correlates with insulin resistance than gynoid fat in transgender individuals. Higher fat mass and insulin resistance in trans women may predispose to increased cardiovascular risk. Despite adverse fat distribution, insulin resistance was not higher in trans men

SOM clustering and modelling of Australian railway drivers’ sleep, wake, duty profiles

Two SOM ANN approaches were used in a study of Australian railway drivers (RDs) to classify RDs’ sleep/wake states and their sleep duration time series profiles over 14 days follow-up. The first approach was a feature-based SOM approach that clustered the most frequently occurring patterns of sleep. The second created RD networks of sleep/wake/duty/break feature parameter vectors of between-states transition probabilities via a multivariate extension of the mixture transition distribution (MTD) model, accommodating covariate interactions. SOM/ANN found 4 clusters of RDs whose sleep profiles differed significantly. Generalised Additive Models for Location, Scale and Shape of the 2 sleep outcomes confirmed that break and sleep onset times, break duration and hours to next duty are significant effects which operate differentially across the groups. Generally sleep increases for next duty onset between 10 am and 4 pm, and when hours since break onset exceeds 1 day. These 2 factors were significant factors determining current sleep, which have differential impacts across the clusters. Some drivers groups catch up sleep after the night shift, while others do so before the night shift. Sleep is governed by the RD’s anticipatory behaviour of next scheduled duty onset and hours since break onset, and driver experience, age and domestic scenario. This has clear health and safety implications for the rail industry. © Springer International Publishing Switzerland 2016

Factors associated with suicide attempts among Australian transgender adults

BACKGROUND: Transgender, including gender diverse and non-binary people, henceforth referred to collectively as trans people, are a highly marginalised population with alarming rates of suicidal ideation, attempted suicide and self-harm. We aimed to understand the risk and protective factors of a lifetime history of attempted suicide in a community sample of Australian trans adults to guide better mental health support and suicide prevention strategies. METHODS: Using a non-probability snowball sampling approach, a total of 928 trans adults completed a cross-sectional online survey between September 2017 and January 2018. The survey assessed demographic data, mental health morbidity, a lifetime history of intentional self-harm and attempted suicide, experiences of discrimination, experiences of assault, access to gender affirming healthcare and access to trans peer support groups. Logistic regression was used to examine the risk or protective effect of participant characteristics on the odds of suicide. RESULTS: Of 928 participants, 85% self-reported a lifetime diagnosis of depression, 63% reported previous self-harm, and 43% had attempted suicide. Higher odds of reporting a lifetime history of suicide attempts were found in people who were; unemployed (adjusted odds ratio (aOR) 1.55 (1.05, 2.29), p = 0.03), had a diagnosis of depression (aOR 3.70 (2.51, 5.45), p < 0.001), desired gender affirming surgery in the future (aOR 1.73 (1.14, 2.61), p = 0.01), had experienced physical assault (aOR 2.01 (1.37, 2.95), p < 0.001) or experienced institutional discrimination related to their trans status (aOR 1.59 (1.14, 2.23), p = 0.007). CONCLUSION: Suicidality is associated with barriers to gender affirming care, gender based victimisation and institutionalised cissexism. Interventions to increase social inclusion, reduce transphobia and enable timely access to gender affirming care, particularly surgical interventions, are potential areas of intervention