Background
Insulin resistance is associated with higher all‐cause and cancer‐specific mortality in postmenopausal women. However, to the authors' knowledge, information regarding insulin resistance ...and breast cancer mortality risk is limited. Therefore, the authors examined associations between insulin resistance and breast cancer incidence and mortality in a subsample of Women's Health Initiative participants.
Methods
A total of 22,837 postmenopausal women with fasting baseline glucose and insulin levels were followed for incident breast cancer and breast cancer mortality. Breast cancers were verified by medical record review and serial National Death Index linkage–enhanced mortality findings. Insulin resistance was estimated using the homeostatic model assessment of insulin resistance (HOMA‐IR). Multivariable Cox proportional hazards models were used to compute hazard ratios (HRs) with 95% confidence intervals (95% CIs) for quartile comparisons. Outcomes included breast cancer incidence, deaths from breast cancer, and deaths after breast cancer (breast cancer followed by death from any cause).
Results
During a median of 19.8 years of follow‐up of 1328 breast cancer cases, there were 512 deaths reported, 151 of which were from breast cancer. Breast cancer incidence was higher in women in the highest HOMA‐IR quartile (HR, 1.34; 95% CI, 1.12‐1.61 P for trend = .003). Although HOMA‐IR was not found to be associated with risk of death from breast cancer (HR, 1.04; 95% CI, 0.60‐1.79), women in the highest versus those in the lowest HOMA‐IR quartile were at a higher risk of death after breast cancer (HR, 1.78; 95% CI, 1.32‐2.39 P for trend <.001).
Conclusions
Higher levels of insulin resistance in postmenopausal women are associated with higher breast cancer incidence and higher all‐cause mortality after breast cancer.
In the current study, insulin resistance is estimated in a cohort of 22,837 postmenopausal women with fasting glucose and insulin determinations. After nearly 20 years of follow‐up, higher levels of insulin resistance appear to be associated with higher breast cancer incidence and higher all‐cause mortality after breast cancer.
Diet is a modifiable component of lifestyle that could influence breast cancer development. The Mediterranean dietary pattern is considered one of the healthiest of all dietary patterns. Adherence to ...the Mediterranean diet protects against diabetes, cardiovascular disease, and cancer. Reported consumption of a Mediterranean diet pattern was associated with lower breast cancer risk for women with all subtypes of breast cancer, and a Western diet pattern was associated with greater risk. In this review, we contrast the available epidemiological breast cancer data, comparing the impact of consuming a Mediterranean diet to the Western diet. Furthermore, we will review the preclinical data highlighting the anticancer molecular mechanism of Mediterranean diet consumption in both cancer prevention and therapeutic outcomes. Diet composition is a major constituent shaping the gut microbiome. Distinct patterns of gut microbiota composition are associated with the habitual consumption of animal fats, high-fiber diets, and vegetable-based diets. We will review the impact of Mediterranean diet on the gut microbiome and inflammation. Outside of the gut, we recently demonstrated that Mediterranean diet consumption led to distinct microbiota shifts in the mammary gland tissue, suggesting possible anticancer effects by diet on breast-specific microbiome. Taken together, these data support the anti-breast-cancer impact of Mediterranean diet consumption.
Dietary supplements are a multi-billion dollar industry in the U.S., and their use is increasing exponentially. Additionally, many foods and beverages are increasingly being fortified with single or ...multiple vitamins and minerals. Consequently, nutrient intakes are exceeding the safe limits established by the Institute of Medicine. In this paper, we examine the benefits and drawbacks of vitamin and mineral supplements and increasing consumption of fortified foods (in addition to dietary intake) in the U.S. population. The pros and cons are illustrated using population estimates of folic acid, calcium, and vitamin D intake, highlighting concerns related to overconsumption of nutrients that should be addressed by regulatory agencies.
Controlling feeding practices, such as pressure to eat, are associated with a child's disinhibited eating and extremes in bodyweight. We aimed to explore which factors are associated with parent ...dyads' pressuring feeding practices, including how mothers and fathers perceive the sharing of household tasks such as mealtime and child feeding responsibilities. In this cross-sectional study, parent dyads (mother and father) of healthy preschool-aged children completed an identical questionnaire consisting of measures of picky eating (food fussiness subscale of Child Eating Behavior Questionnaire), parental concern for undereating, and pressure to eat (Child Feeding Questionnaire). We used separate multivariable linear regression models for mothers and fathers to assess correlates associated with pressure to eat subscale score, including slowness of eating and enjoyment of food, child BMI z-score and race/ethnicity, and household income. Separate unadjusted linear regression models for mothers and fathers were used to report the association of pressure to eat with household responsibilities. Parents (N = 88) had similar mean picky eating, concern for undereating, and pressure to eat scores; more fathers had high pressure to eat scores (36% vs 27%). Higher pressure to eat was significantly associated with lower income, non-Hispanic Black or Black race/ethnicity, slow eating, and lower enjoyment of food. Pressure was not associated with household responsibilities. While there were similar maternal and paternal perceptions of child eating behaviors, more fathers reported pressuring their child to eat. Identifying differences in parental feeding practices may assist in intervention development to improve feeding practices.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Diet modulates inflammation and inflammatory markers have been associated with cancer outcomes. In the Women's Health Initiative, we investigated associations between a dietary inflammatory index ...(DII) and invasive breast cancer incidence and death.
The DII was calculated from a baseline food frequency questionnaire in 122 788 postmenopausal women, enrolled from 1993 to 1998 with no prior cancer, and followed until 29 August 2014. With median follow-up of 16.02 years, there were 7495 breast cancer cases and 667 breast cancer deaths. We used Cox regression to estimate multivariable-adjusted hazards ratios (HRs) and 95% confidence intervals (95% CIs) by DII quintiles (Q) for incidence of overall breast cancer, breast cancer subtypes, and deaths from breast cancer. The lowest quintile (representing the most anti-inflammatory diet) was the reference.
The DII was not associated with incidence of overall breast cancer (HRQ5vsQ1, 0.99; 95% CI, 0.91-1.07; Ptrend=0.83 for overall breast cancer). In a full cohort analysis, a higher risk of death from breast cancer was associated with consumption of more pro-inflammatory diets at baseline, after controlling for multiple potential confounders (HRQ5vsQ1, 1.33; 95% CI, 1.01-1.76; Ptrend=0.03).
Future studies are needed to examine the inflammatory potential of post-diagnosis diet given the suggestion from the current study that dietary inflammatory potential before diagnosis is related to breast cancer death.
Background
Numerous studies have examined if food insecurity (FI) leads to increased weight gain, but little is known about how FI affects obese participants.
Objective
Our objective was to determine ...if obese, food-insecure adults are more likely to have medical comorbidities than obese, food-secure adults.
Design
We conducted a cross-sectional study using the 2007–2014 National Health and Nutrition Examination Survey (NHANES).
Participants
All obese participants (≥ 20 years) in NHANES were eligible. Participants who were pregnant or missing FI data were excluded.
Main Measures
The primary exposure was household FI, and the primary outcome was the total number of obesity-related comorbidities. Secondary outcomes evaluated the association between FI and individual comorbidities. Propensity score weighting was used to improve covariate balance. We used negative binomial regression to test the association between FI and the total number of comorbidities. We used logistic regression to test the association between FI and individual comorbidities.
Key Results
Of the 9203 obese participants, 15.6% were food insecure. FI (
β
= 0.09, 95% CI: 0.02, 0.15;
p
= 0.01) and very low food security (
β
= 0.17, 95% CI: 0.07, 0.28;
p
= 0.003) were associated with an increased number of comorbidities. In secondary analyses, FI was associated with increased odds of coronary artery disease (OR: 1.5, 95% CI: 1.1, 2.0) and asthma (OR: 1.3, 95% CI: 1.1, 1.6). Very low food security was associated with increased odds of coronary artery disease, diabetes, and asthma.
Conclusion
Obese adults living in food-insecure households were more likely to have an increased number of comorbid conditions than obese adults living in food-secure households. Clinicians should be aware of the association between FI and comorbid medical conditions when treating patients with obesity.
To assess the extent of error present in self-reported weight data in the Women's Health Initiative, variables that may be associated with error, and to develop methods to reduce any identified ...error.
Prospective cohort study.
Forty clinical centres in the USA.ParticipantsWomen (n 75 336) participating in the Women's Health Initiative Observational Study (WHI-OS) and women (n 6236) participating in the WHI Long Life Study (LLS) with self-reported and measured weight collected about 20 years later (2013-2014).
The correlation between self-reported and measured weights was 0·97. On average, women under-reported their weight by about 2 lb (0·91 kg). The discrepancies varied by age, race/ethnicity, education and BMI. Compared with normal-weight women, underweight women over-reported their weight by 3·86 lb (1·75 kg) and obese women under-reported their weight by 4·18 lb (1·90 kg) on average. The higher the degree of excess weight, the greater the under-reporting of weight. Adjusting self-reported weight for an individual's age, race/ethnicity and education yielded an identical average weight to that measured.
Correlations between self-reported and measured weights in the WHI are high. Discrepancies varied by different sociodemographic characteristics, especially an individual's BMI. Correction of self-reported weight for individual characteristics could improve the accuracy of assessment of obesity status in postmenopausal women.
Low- and middle-income countries (LMICs) are experiencing major increases in diabetes and cardiovascular conditions linked to overweight and obesity. Lifestyle interventions such as the United States ...National Diabetes Prevention Program (DPP) developed in high-income countries require adaptation and cultural tailoring for LMICs. The objective of this study was to evaluate the efficacy of "Lifestyle Africa," an adapted version of the DPP tailored for an underresourced community in South Africa compared to usual care.
Participants were residents of a predominantly Xhosa-speaking urban township of Cape Town, South Africa characterized by high rates of poverty. Participants with body mass index (BMI) ≥ 25 kg/m2 who were members of existing social support groups or "clubs" receiving health services from local nongovernmental organizations (NGOs) were enrolled in a cluster randomized controlled trial that compared Lifestyle Africa (the intervention condition) to usual care (the control condition). The Lifestyle Africa intervention consisted of 17 video-based group sessions delivered by trained community health workers (CHWs). Clusters were randomized using a numbered list of the CHWs and their assigned clubs based on a computer-based random allocation scheme. CHWs, participants, and research team members could not be blinded to condition. Percentage weight loss (primary outcome), hemoglobin A1c (HbA1c), blood pressure, triglycerides, and low-density lipoprotein (LDL) cholesterol were assessed 7 to 9 months after enrollment. An individual-level intention-to-treat analysis was conducted adjusting for clustering within clubs and baseline values. Trial registration is at ClinicalTrials.gov (NCT03342274). Between February 2018 and May 2019, 782 individuals were screened, and 494 were enrolled. Participants were predominantly retired (57% were receiving a pension) and female (89%) with a mean age of 68 years. Participants from 28 clusters were allocated to Lifestyle Africa (15, n = 240) or usual care (13, n = 254). Fidelity assessments indicated that the intervention was generally delivered as intended. The modal number of sessions held across all clubs was 17, and the mean attendance of participants across all sessions was 61%. Outcome assessment was completed by 215 (90%) intervention and 223 (88%) control participants. Intent-to-treat analyses utilizing multilevel modeling included all randomized participants. Mean weight change (primary outcome) was -0.61% (95% confidence interval (CI) = -1.22, -0.01) in Lifestyle Africa and -0.44% (95% CI = -1.06, 0.18) in control with no significant difference (group difference = -0.17%; 95% CI = -1.04, 0.71; p = 0.71). However, HbA1c was significantly lower at follow-up in Lifestyle Africa compared to the usual care group (mean difference = -0.24, 95% CI = -0.39, -0.09, p = 0.001). None of the other secondary outcomes differed at follow-up: systolic blood pressure (group difference = -1.36; 95% CI = -6.92, 4.21; p = 0.63), diastolic blood pressure (group difference = -0.39; 95% CI = -3.25, 2.30; p = 0.78), LDL (group difference = -0.07; 95% CI = -0.19, 0.05; p = 0.26), triglycerides (group difference = -0.02; 95% CI = -0.20, 0.16; p = 0.80). There were no unanticipated problems and serious adverse events were rare, unrelated to the intervention, and similar across groups (11 in Lifestyle Africa versus 13 in usual care). Limitations of the study include the lack of a rigorous dietary intake measure and the high representation of older women.
In this study, we found that Lifestyle Africa was feasible for CHWs to deliver and, although it had no effect on the primary outcome of weight loss or secondary outcomes of blood pressure or triglycerides, it had an apparent small significant effect on HbA1c. The study demonstrates the potential feasibility of CHWs to deliver a program without expert involvement by utilizing video-based sessions. The intervention may hold promise for addressing cardiovascular disease (CVD) and diabetes at scale in LMICs.
ClinicalTrials.gov NCT03342274.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This report provides a further analysis of the first year weight losses in the Look AHEAD (Action for Health in Diabetes) study and identifies factors associated with success. Participants were a ...total of 5,145 men and women with type 2 diabetes who were recruited at 16 sites and randomly assigned to an intensive lifestyle intervention (ILI) or a control condition, Diabetes Support and Education (DSE). During year 1, participants in ILI received comprehensive diet and physical activity counseling in a total of 42 group and individual sessions, compared with three educational sessions for DSE participants. As reported previously, at the end of the year, ILI participants lost 8.6% of initial weight, compared to 0.7% for DSE (P < 0.001). Within the ILI group, all racial/ethnic groups achieved clinically significant weight losses (>5.5%), although there were significant differences among groups. For the year, ILI participants attended an average of 35.4 treatment sessions and reported exercising a mean of 136.6 min/week and consuming a total of 360.9 meal replacement products. Greater self‐reported physical activity was the strongest correlate of weight loss, followed by treatment attendance and consumption of meal replacements. The use of orlistat, during the second half of the year, increased weight loss only marginally in those ILI participants who had lost <5% of initial weight during the first 6 months and chose to take the medication thereafter as a toolbox option. The lifestyle intervention was clinically effective in all subsets of an ethnically and demographically diverse population.
To determine how baseline weight status contributes to differences in postmenopausal weight gain among non-Hispanic Blacks (NHBs) and non-Hispanic Whites (NHWs).
Data were included from 70,750 NHW ...and NHB postmenopausal women from the Women's Health Initiative Observational Study (WHI OS). Body Mass Index (BMI) at baseline was used to classify women as having normal weight, overweight, obese class I, obese class II or obese class III. Cox proportional hazards was used to estimate the hazard of a 10% or more increase in weight from baseline.
In both crude and adjusted models, NHBs were more likely to experience ≥10% weight gain than NHWs within the same category of baseline weight status. Moreover, NHBs who were normal weight at baseline were most likely to experience ≥10% weight gain in both crude and adjusted models. Age-stratified results were consistent with overall findings. In all age categories, NHBs who were normal weight at baseline were most likely to experience ≥10% weight gain. Based on the results of adjusted models, the joint influence of NHB race/ethnicity and weight status on risk of postmenopausal weight gain was both sub-additive and sub-multiplicative.
NHBs are more likely to experience postmenopausal weight gain than NHWs, and the disparity in risk is most pronounced among those who are normal weight at baseline. To address the disparity in postmenopausal obesity, future studies should focus on identifying and modifying factors that promote weight gain among normal weight NHBs.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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