Macronutrients and the risk of premenstrual syndrome

Premenstrual syndrome (PMS) affects 8-20% of reproductive-aged women, impacting work, family, and social interactions. Limitations in available PMS treatments, including side effects and limited medication efficacy, indicate the need for improved prevention. Modifiable risk factors for prevention of PMS include dietary factors. Several micronutrients have been identified as risk factors, but there has been little evaluation of macronutrients. Thus, the research aim was to examine prospectively whether macronutrient consumption was associated with PMS development among a subset of women enrolled in the Nurses’ Health Study II cohort. Chapter 1 evaluates the association of fat intake and PMS risk. Among 3,638 women, total fat intake was not associated with PMS risk, but stearic acid was associated with a 25% decrease risk of PMS. As this was the first study to observe this association, the finding needs to be replicated. Chapter 2 assesses intake of carbohydrates and PMS risk. Overall, carbohydrate intake was not associated with PMS risk but maltose was associated with a 45% increased risk of PMS. Again, this is the first study to find this and replication is needed. Chapter 3 evaluates intake of protein and PMS risk. Protein intake was not associated with PMS risk. Additionally, substitution of macronutrients for each other did not suggest that any macronutrient was importantly associated with PMS risk. In conclusion, macronutrient intake was not associated with risk of developing PMS after controlling for micronutrient intake and other potential confounders. Micronutrients may play a more important role in PMS development than macronutrient intake

Social Support for Changing Multiple Behaviors: Factors Associated With Seeking Support and the Impact of Offered Support

Introduction. Social support is important for behavior change, and it may be particularly important for the complexities of changing multiple risk behaviors (MRB). Research is needed to determine if participants in an MRB intervention can be encouraged to activate their social network to aid their change efforts. Methods. Healthy Directions 2, a cluster-randomized controlled trial of an intervention conducted in two urban health centers, targeted five behaviors (physical activity, fruit and vegetable intake, red meat consumption, multivitamin use, and smoking). The self-guided intervention emphasized changing MRB simultaneously, focused on self-monitoring and action planning, and encouraged participants to seek support from social network members. An MRB score was calculated for each participant, with one point being assigned for each behavioral recommendation that was not met. Analyses were conducted to identify demographic and social contextual factors (e.g., interpersonal, neighborhood, and organizational resources) associated with seeking support and to determine if type and frequency of offered support were associated with changes in MRB score. Results. Half (49.6%) of participants identified a support person. Interpersonal resources were the only contextual factor that predicted engagement of a support person. Compared to individuals who did not seek support, those who identified one support person had 61% greater reduction in MRB score, and participants identifying multiple support persons had 100% greater reduction. Conclusion. Engagement of one’s social network leads to significantly greater change across multiple risk behaviors. Future research should explore strategies to address support need for individuals with limited interpersonal resources

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Recreational Physical Activity and Premenstrual Syndrome in Young Adult Women: A Cross-Sectional Study.

It is estimated that up to 75% of premenopausal women experience at least one premenstrual symptom and 8-20% meet clinical criteria for premenstrual syndrome. Premenstrual syndrome substantially reduces quality of life for many women of reproductive age, with pharmaceutical treatments having limited efficacy and substantial side effects. Physical activity has been recommended as a method of reducing menstrual symptom severity. However, this recommendation is based on relatively little evidence, and the relationship between physical activity, premenstrual symptoms, and premenstrual syndrome remains unclear.We evaluated the relationship between physical activity and premenstrual syndrome and premenstrual symptoms among 414 women aged 18-31. Usual premenstrual symptom experience was assessed with a modified version of the Calendar of Premenstrual Experiences. Total, physical, and affective premenstrual symptom scores were calculated for all participants. Eighty women met criteria for moderate-to-severe premenstrual syndrome, while 89 met control criteria. Physical activity, along with dietary and lifestyle factors, was assessed by self-report.Physical activity was not significantly associated with total, affective, or physical premenstrual symptom score. Compared to the women with the lowest activity, women in tertiles 2 and 3 of activity, classified as metabolic equivalent task hours, had prevalence odds ratios for premenstrual syndrome of 1.5 (95% CI: 0.6-3.7) and 0.9 (95% CI: 0.4-2.4), respectively (p-value for trend = 0.85).We found no association between physical activity and either premenstrual symptom scores or the prevalence of premenstrual syndrome

Association between METs^* and premenstrual symptom scores, among all participants (n = 414).

<p>Association between METs^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169728#t003fn001" target="_blank">*</a>} and premenstrual symptom scores, among all participants (n = 414).</p

Odds ratios and confidence intervals for the association of physical activity and PMS^*.

<p>Odds ratios and confidence intervals for the association of physical activity and PMS^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169728#t004fn001" target="_blank">*</a>}.</p

Severity of premenstrual symptoms^* of all study participants (n = 414) and of women meeting PMS case (n = 80) and control criteria (n = 89).

<p>Severity of premenstrual symptoms^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169728#t002fn001" target="_blank">*</a>} of all study participants (n = 414) and of women meeting PMS case (n = 80) and control criteria (n = 89).</p

Characteristics of study participants (n = 414) and comparison between PMS cases (n = 80) and controls (n = 89)^*.

<p>Characteristics of study participants (n = 414) and comparison between PMS cases (n = 80) and controls (n = 89)^{<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169728#t001fn001" target="_blank">*</a>}.</p