Does Insulin Delivery Technology Change Our Relationship with Foods? A Scoping Review.

Introduction: Automated insulin delivery (AID) systems reduce burden and improve glycemic management for people with type 1 diabetes (PwT1D) by automatically adjusting insulin as a response to measured glucose levels. There is a lack of evidence on AID and nutrition variables such as dietary intake, eating behaviors, and disordered eating. Objectives: This scoping review aims to provide a summary of the literature regarding AID and nutrition variables and to identify gaps that require further investigation. Methods: Two researchers conducted a blinded search of Medline (OVID) and PubMed for studies, including AID use (compared to non-AID use) and nutrition variables. Studies from January 2000 to July 2023 were included, as were PwT1D of all ages. Results: A total of 3132 articles were screened for appropriateness. After exclusions, 7 studies were included (2017-2023): 4 qualitative, 1 crossover, 1 randomized controlled, and 1 observational. Studies included adolescents (n = 1), adults (n = 3) or both (n = 2), and all ages (n = 1). In quantitative studies, AID was associated with lower eating distress (-0.43 ± 0.12, P = 0.004) and higher quality of life (3.1, 95% confidence interval [CI]: 0.8-5.4, P = 0.01), but not grams of carbohydrates at meals (1.0; 95% CI: -0.7 to 3.0; P = 0.24) and snacks (0.004; 95% CI: -0.8 to 0.8; P = 0.99) compared to non-AID use. In qualitative studies, AID increased the frequency and portions of food intake and led to less dietary control from parents. AID users reported eating foods higher in energy density. PwT1D were less worried about achieving accurate carbohydrate counting (CC) when using AID. Conclusions: AID use appears to influence eating behaviors, dietary patterns, and CC, although evidence was limited. AID may reduce food management burden due to the perception that AID can correct for CC inaccuracy. Significance: Further research needs to determine if AID allows for simplification of CC and improves eating behaviors while maintaining glycemic stability.

Effect of diabetes technologies on the fear of hypoglycaemia among people living with type 1 diabetes: a systematic review and meta-analysis.

Background: Fear of hypoglycaemia (FOH) significantly disrupts the daily management of type 1 diabetes (T1D) and increases the risk of complications. Recent technological advances can improve glucose metrics and reduce hypoglycaemia frequency, yet their impact on FOH is unclear. This systematic review and meta-analysis (SRMA) aimed to synthesize the current literature to understand the impact of diabetes technologies on FOH in T1D. Methods: In this SRMA, we searched PubMed, Medline, Scopus, and Web of Science from inception up to May 21st, 2023 for studies assessing the effect of using real-time or intermittently scanned continuous glucose monitors (rtCGM or isCGM); insulin pumps (CSII); and their combinations on FOH as the primary outcome, measured using the Hypoglycaemia Fear Survey (HFS; including total, worries [HFS-W], and behaviours [HFS-B] scores), in non-pregnant adults with T1D. Data was extracted by the first and second authors. Results were pooled using a random-effects model based on study design (RCT and non-RCT), with subgroup analysis based on the type of technology, reported change in hypoglycaemia frequency, and duration of use. Risk of bias was evaluated with Cochrane and Joanna Briggs Institute tools. This study is registered with PROSPERO, CRD42021253618. Findings: A total of 51 studies (n = 8966) were included, 22 of which were RCTs. Studies on rtCGM and CSII reported lower FOH levels with ≥8 weeks of use. Studies on CSII and rtCGM combinations reported lower FOH levels after ≥13 weeks of automated insulin delivery (AID) use or 26 weeks of sensor-augmented pump (SAP) use. The meta-analysis showed an overall lower FOH with technologies, specifically for the HFS-W subscale. The RCT meta-analysis showed lower HFS-W scores with rtCGM use (standard mean difference [95%CI]: -0.14 [-0.23, -0.05], I2 = 0%) and AID (-0.17 [-0.33, -0.01], I2 = 0%). Results from non-RCT studies show that SAP users (-0.33 [-0.38, -0.27], I2 = 0%) and rtCGM users (-0.38 [-0.61, -0.14], I2 = 0%) had lower HFS-W. Interpretation: We found consistent, yet small to moderate, effects supporting that diabetes technologies (specifically rtCGM, SAP, and AID) may reduce hypoglycaemia-related worries in adults with T1D. Current literature, however, has limitations including discrepancies in baseline characteristics and limited, mainly descriptive, statistical analysis. Thus, future studies should assess FOH as a primary outcome, use validated surveys, and appropriate statistical analysis to evaluate the clinical impacts of technology use beyond just glucose metrics. Funding: Canadian Institutes of Health Research, Juvenile Diabetes Research Foundation Ltd.

Are nocturnal hypoglycemia prevention strategies influenced by diabetes technology usage? A BETTER registry analysis.

AIM: To assess the association of nocturnal hypoglycemia prevention strategies (NH-PS) and diabetes technology usage (insulin pump and/or continuous glucose monitors [CGM]) in people with type 1 diabetes (PWT1D). METHODS: Logistic regression models were used to describe associations between self-reported NH-PS and diabetes technology (pump with intermittently-scanned or real-time CGM (isCGM or rtCGM), or automated insulin delivery (AID)), hypoglycemia history, and fear of hypoglycemia (FOH). RESULTS: Among 831 adults (65 % female, aged 44 ± 15 years, T1D duration 26 ± 15 years), 32 % reported HbA1c ≤ 7.0 %, 88 % used ≥ 1 diabetes technology, 66 % reported ≥ 1 symptomatic NH in the past month, and 64 % used ≥ 2 NH-PS. Compared to multiple daily injections (MDI) + capillary blood glucose (CBG), bedtime snack consumption was less likely among pump + isCGM (OR [95 %CI]: 0.55 [0.31, 0.98]), pump + rtCGM (0.40 [0.20, 0.81]), and AID (0.34 [0.17, 0.66]) users, while evening insulin basal reduction was associated with CSII + CBG (3.15 [1.25, 7.99]), pump + isCGM 4.00 [1.99, 8.01]), and pump + rtCGM 2.89 [1.28, 6.50] use. Elevated FOH was associated with snack consumption (1.37 [1.00, 1.89]), evening bolus insulin avoidance (1.77 [1.11, 2.83]), limiting exercise (2.50 [1.30, 4.82]), and limiting alcohol consumption (2.33 [1.15, 4.70]) as NH-PS. CONCLUSION: Technology use and elevated FOH might influence PWT1D' choice of NH-PS.

Clinical Prediction Models for Heart Failure Hospitalization in Type 2 Diabetes: A Systematic Review and Meta-Analysis.

Background Clinical prediction models have been developed for hospitalization for heart failure in type 2 diabetes. However, a systematic evaluation of these models' performance, applicability, and clinical impact is absent. Methods and Results We searched Embase, MEDLINE, Web of Science, Google Scholar, and Tufts' clinical prediction registry through February 2021. Studies needed to report the development, validation, clinical impact, or update of a prediction model for hospitalization for heart failure in type 2 diabetes with measures of model performance and sufficient information for clinical use. Model assessment was done with the Prediction Model Risk of Bias Assessment Tool, and meta-analyses of model discrimination were performed. We included 15 model development and 3 external validation studies with data from 999 167 people with type 2 diabetes. Of the 15 models, 6 had undergone external validation and only 1 had low concern for risk of bias and applicability (Risk Equations for Complications of Type 2 Diabetes). Seven models were presented in a clinically useful manner (eg, risk score, online calculator) and 2 models were classified as the most suitable for clinical use based on study design, external validity, and point-of-care usability. These were Risk Equations for Complications of Type 2 Diabetes (meta-analyzed c-statistic, 0.76) and the Thrombolysis in Myocardial Infarction Risk Score for Heart Failure in Diabetes (meta-analyzed c-statistic, 0.78), which was the simplest model with only 5 variables. No studies reported clinical impact. Conclusions Most prediction models for hospitalization for heart failure in patients with type 2 diabetes have potential concerns with risk of bias or applicability, and uncertain external validity and clinical impact. Future research is needed to address these knowledge gaps.

Sex Differences in the Risk of Coronary Heart Disease Associated With Type 2 Diabetes: A Mendelian Randomization Analysis.

OBJECTIVE: Observational studies have demonstrated that type 2 diabetes is a stronger risk factor for coronary heart disease (CHD) in women compared with men. However, it is not clear whether this reflects a sex differential in the causal effect of diabetes on CHD risk or results from sex-specific residual confounding. RESEARCH DESIGN AND METHODS: Using 270 single nucleotide polymorphisms (SNPs) for type 2 diabetes identified in a type 2 diabetes genome-wide association study, we performed a sex-stratified Mendelian randomization (MR) study of type 2 diabetes and CHD using individual participant data in UK Biobank (251,420 women and 212,049 men). Weighted median, MR-Egger, MR-pleiotropy residual sum and outlier, and radial MR from summary-level analyses were used for pleiotropy assessment. RESULTS: MR analyses showed that genetic risk of type 2 diabetes increased the odds of CHD for women (odds ratio 1.13 [95% CI 1.08-1.18] per 1-log unit increase in odds of type 2 diabetes) and men (1.21 [1.17-1.26] per 1-log unit increase in odds of type 2 diabetes). Sensitivity analyses showed some evidence of directional pleiotropy; however, results were similar after correction for outlier SNPs. CONCLUSIONS: This MR analysis supports a causal effect of genetic liability to type 2 diabetes on risk of CHD that is not stronger for women than men. Assuming a lack of bias, these findings suggest that the prevention and management of type 2 diabetes for CHD risk reduction is of equal priority in both sexes.

Exercise in Pregnant Women with Diabetes.

PURPOSE OF REVIEW: Diabetes affects an increasing number of pregnancies. Regular exercise is recommended for pregnant women without diabetes, but whether exercise during pregnancy also benefits women with gestational diabetes (GDM) or preexisting (type 1 or type 2) diabetes or if these women have any specific risks is unclear. RECENT FINDINGS: Recent evidence suggests that low- to moderate-intensity exercise improves blood glucose and may delay insulin initiation for women with GDM. Exercise is also safe, with no reports of increased maternal or neonatal complications. Few studies evaluated exercise as adjunct therapy for pregnant women with preexisting diabetes, precluding a thorough assessment in this population. Low- to moderate-intensity exercise during pregnancy safely improves glycemic control among women with GDM. More studies are needed to evaluate the impact of exercise in pregnant women with preexisting diabetes. Whether a specific type, volume, or timing of activity is most effective is not known.

Objectively measured physical activity and plasma metabolomics in the Shanghai Physical Activity Study.

BACKGROUND: Physical activity is associated with a variety of health benefits, but the biological mechanisms that explain these associations remain unclear. Metabolomics is a powerful tool to comprehensively evaluate global metabolic signature associated with physical activity and helps to pinpoint the pathways that mediate the health effects of physical activity. There has been limited research on metabolomics and habitual physical activity, and no metabolomics study has examined sedentary behaviour and physical activity of different intensities. METHODS: In a group of Chinese adults (N = 277), we used an untargeted approach to examine 328 plasma metabolites in relation to accelerometer-measured physical activity, including overall volume of physical activity (physical activity energy expenditure (PAEE) and duration of physically active time) and sedentary time, and measures related to different intensities of physical activity (moderate-to-vigorous activity (MVPA), light activity, average physical activity intensity). RESULTS: We identified 11 metabolites that were associated with total activity, with a false discovery rate of 0.2 or lower. Notably, we observed generally lower levels of amino acids in the valine, leucine and isoleucine metabolism pathway and of carbohydrates in sugar metabolism among participants with higher activity levels. Moreover, we found that PAEE, time spent in light activity and duration of physically active time were associated with a similar metabolic pattern, whereas the metabolic signature associated with sedentary time mirrored this pattern. In contrast, average activity intensity and time spent in MVPA appeared to be associated with somewhat different metabolic patterns. CONCLUSIONS: Overall, the metabolomics patterns support a beneficial role of higher volume of physical activity in cardiometabolic health. Our findings identified candidate pathways and provide insight into the mechanisms underlying the health effects of physical activity.

Accelerometer-measured physical activity in Chinese adults.

BACKGROUND: Following adoption of a Western lifestyle, China is experiencing a decline in physical activity levels, which is projected to contribute to future increases in the burden of chronic diseases. PURPOSE: This study aims to target public health interventions and identify personal characteristics associated with physical activity and sedentary behavior in urban Chinese adults. METHODS: In a sample of 576 men and women aged 40-74 years from Shanghai, multiple logistic regression was used to examine demographic, anthropometric, and lifestyle factors in relation to levels of physical activity and sedentary behavior assessed by Actigraph accelerometers. RESULTS: Participants spent 317 minutes/day in physical activity and 509 minutes/day sedentary. In multivariate models, people aged > or =60 years were significantly less likely than those aged <50 years to engage in physical activity (OR=0.29, 95% CI=0.17, 0.49) and more likely to spend time sedentary (OR=2.77, 95% CI=1.53, 5.05). Similarly, obese individuals were less likely to be physically active (OR=0.34, 95% CI=0.17, 0.66) and they were suggestively more likely to be sedentary (OR=1.87, 95% CI=0.94, 3.71) than normal-weight individuals. Furthermore, current cigarette smokers were less physically active than those who formerly or never smoked (OR=0.47, 95% CI=0.28, 0.78). CONCLUSIONS: Physical activity promotion programs in urban China should target older people, obese individuals, and cigarette smokers, as these population subgroups exhibited low levels of physical activity.

Validity of a physical activity questionnaire in Shanghai.

PURPOSE: In large epidemiologic studies, physical activity (PA) is often assessed using PA questionnaires (PAQ). Because available PAQ may not capture the full range of PA in which urban Chinese adults engage, a PAQ was developed for this purpose. We examined the validity of this PAQ and the 1-yr stability of PA in 545 urban Shanghai adults. METHODS: The PAQ was interview-administered twice, approximately 1 yr apart, and participants also wore an accelerometer and completed a PA-log for seven consecutive days every 3 months during the same year. The intraclass correlation coefficient (ICC) was used to evaluate the stability of PA across questionnaire administrations, and Spearman correlation coefficients (ρ) and mean differences and 95% limits of agreement were used to examine the validity of the questionnaire compared against accelerometry and the PA-log. RESULTS: When measured by accelerometry, estimates of time spent in moderate-to-vigorous PA were lower and estimates of time spent sedentary were higher than when self-reported on the PAQ (P < 0.001). Total PA (ICC = 0.65) and PA domains (ICC = 0.45-0.85) showed moderate to high stability across PAQ administrations. Total PA (ρ = 0.30), moderate-to-vigorous activity (ρ = 0.17), light activity (ρ = 0.36), and sedentary behavior (ρ = 0.16) assessed by PAQ and by accelerometry were significantly and positively correlated, and correlations of the PAQ with the PA-log (ρ = 0.36-0.85) were stronger than those observed with accelerometry. CONCLUSIONS: The PAQ significantly overestimated time spent in moderate-to-vigorous activity and underestimated time spent in light activity and sedentary behavior compared with accelerometry, but it performed well at ranking participants according to PA level.

Intensity and timing of physical activity in relation to postmenopausal breast cancer risk: the prospective NIH-AARP diet and health study.

BACKGROUND: Despite strong evidence of an inverse association of physical activity with postmenopausal breast cancer risk, whether a certain intensity or time of life of physical activity is most effective for lowering breast cancer risk is not known. METHODS: In 118,899 postmenopausal women in the prospective NIH-AARP Diet and Health Study, we examined the relations of light and moderate-to-vigorous intensity physical activity during four periods of life ("historical": ages 15-18, 19-29, 35-39 years; "recent": past 10 years) to postmenopausal breast cancer risk. Physical activity was assessed by self-report at baseline, and 4287 incident breast cancers were identified over 6.6 years of follow-up. RESULTS: In age-adjusted and multivariate Cox regression models, >7 hours/week of moderate-to-vigorous activity during the past 10 years was associated with 16% reduced risk of postmenopausal breast cancer (RR:0.84; 95%CI:0.76,0.93) compared with inactivity. The association remained statistically significant after adjustment for BMI (RR:0.87; 95%CI:0.78,0.96). Neither moderate-to-vigorous activity during other periods of life nor light intensity activity during any period of life was related to breast cancer risk, and associations did not vary by tumor characteristics. CONCLUSION: A high level of recent, but not historical, physical activity of moderate-to-vigorous intensity is associated with reduced postmenopausal breast cancer risk. More precise recall of recent physical activity than activity in the distant past is one possible explanation for our findings.

Age-specific physical activity and prostate cancer risk among white men and black men.

BACKGROUND: : The relation of physical activity across the lifespan to risk of prostate cancer has not been thoroughly investigated, particularly among black men. The authors investigated physical activity, including activity during different age periods and of various intensities, in relation to prostate cancer incidence among white men and black men. METHODS: : In total, 160,006 white men and 3671 black men ages 51 years to 72 years who were enrolled in the National Institutes of Health-AARP Diet and Health Study reported their time spent per week engaging in physical activity during ages 15 to 18 years, 19 years to 29 years, 35 years to 39 years, and during the past 10 years. Cox regression models were used to examine physical activity, categorized by intensity (moderate or vigorous, light, and total), in relation to prostate cancer risk. RESULTS: : During 7 years of follow-up, 9624 white men and 371 black men developed prostate cancer. Among white men, physical activity had no association with prostate cancer regardless of age period or activity intensity. Among black men, engaging in > or =4 hours of moderate/vigorous intensity physical activity versus infrequent activity during ages 19 years to 29 years was related to a 35% lower risk of prostate cancer (relative risk, 0.65; 95% confidence interval [95% CI], 0.43-0.99 [P(trend) = .01]). Frequent moderate/vigorous physical activity at ages 35 years to 39 years also potentially was related to reduced prostate cancer risk (relative risk, 0.59; 95% CI, 0.36-0.96 [P(trend) = .15]). CONCLUSIONS: : Regular physical activity may reduce prostate cancer risk among black men, and activity during young adulthood may yield the greatest benefit. This novel finding needs confirmation in additional studies. Cancer 2009. Published 2009 by the American Cancer Society.

Physical activity and postmenopausal breast cancer risk in the NIH-AARP diet and health study.

BACKGROUND: Although physical activity has been associated with reduced breast cancer risk, whether this association varies across breast cancer subtypes or is modified by reproductive and lifestyle factors is unclear. METHODS: We examined physical activity in relation to postmenopausal breast cancer risk in 182,862 U.S. women in the NIH-AARP Diet and Health Study. Physical activity was assessed by self-report at baseline (1995-1996), and 6,609 incident breast cancers were identified through December 31, 2003. Cox regression was used to estimate the relative risk (RR) and 95% confidence interval (95% CI) of postmenopausal breast cancer overall and by tumor characteristics. Effect modification by select reproductive and lifestyle factors was also explored. RESULTS: In multivariate models, the most active women experienced a 13% lower breast cancer risk versus inactive women (RR, 0.87; 95% CI, 0.81-0.95). This inverse relation was not modified by tumor stage or histology but was suggestively stronger for estrogen receptor (ER)-negative (RR, 0.75; 95% CI, 0.54-1.04) than ER-positive (RR, 0.97; 95% CI, 0.84-1.12) breast tumors and was suggestively stronger for overweight/obese (RR, 0.86; 95% CI, 0.77-0.96) than lean (RR, 0.95; 95% CI, 0.87-1.05) women. The inverse relation with physical activity was also more pronounced among women who had never used menopausal hormone therapy and those with a positive family history of breast cancer than their respective counterparts. CONCLUSIONS: Physical activity was associated with reduced postmenopausal breast cancer risk, particular to ER-negative tumors. These results, along with heterogeneity in the physical activity-breast cancer relation for subgroups of menopausal hormone therapy use and adiposity, indicate that physical activity likely influences breast cancer risk via both estrogenic and estrogen-independent mechanisms.

Prospective study of physical activity and risk of postmenopausal breast cancer.

INTRODUCTION: To prospectively examine the relation of total, vigorous and non-vigorous physical activity to postmenopausal breast cancer risk. METHODS: We studied 32,269 women enrolled in the Breast Cancer Detection Demonstration Project Follow-up Study. Usual physical activity (including household, occupational and leisure activities) throughout the previous year was assessed at baseline using a self-administered questionnaire. Postmenopausal breast cancer cases were identified through self-reports, death certificates and linkage to state cancer registries. A Cox proportional hazards regression was used to estimate the relative risk and 95% confidence intervals of postmenopausal breast cancer associated with physical activity. RESULTS: During 269,792 person-years of follow-up from 1987 to 1998, 1506 new incident cases of postmenopausal breast cancer were ascertained. After adjusting for potential risk factors of breast cancer, a weak inverse association between total physical activity and postmenopausal breast cancer was suggested (relative risk comparing extreme quintiles = 0.87; 95% confidence interval = 0.74 to 1.02; p for trend = 0.21). That relation was almost entirely contributed by vigorous activity (relative risk comparing extreme categories = 0.87; 95% confidence interval = 0.74 to 1.02; p for trend = 0.08). The inverse association with vigorous activity was limited to women who were lean (ie, body mass index <25.0 kg/m2: relative risk = 0.68; 95% confidence interval = 0.54 to 0.85). In contrast, no association with vigorous activity was noted among women who were overweight or obese (ie, body mass index > or = 25.0 kg/m2: relative risk = 1.18; 95% confidence interval = 0.93 to 1.49; p for interaction = 0.008). Non-vigorous activity showed no relation to breast cancer (relative risk comparing extreme quintiles = 1.02; 95% confidence interval = 0.87 to 1.19; p for trend = 0.86). The physical activity and breast cancer relation was not specific to a certain hormone receptor subtype. CONCLUSIONS: In this cohort of postmenopausal women, breast cancer risk reduction appeared to be limited to vigorous forms of activity; it was apparent among normal weight women but not overweight women, and the relation did not vary by hormone receptor status. Our findings suggest that physical activity acts through underlying biological mechanisms that are independent of body weight control.

Physical activity in relation to total, advanced, and fatal prostate cancer.

Physical activity has been inconsistently related to total prostate cancer and few studies have examined whether this association varies by disease aggressiveness. We examined physical activity in relation to total, advanced, and fatal prostate cancer in the NIH-AARP Diet and Health Study. At baseline (1995-1996), 293,902 men ages 50 to 71 years completed a questionnaire inquiring about current frequency of vigorous exercise of at least 20 min of duration, as well as frequency of exercise during adolescence (ages 15-18). We used proportional hazards regression to calculate multivariate relative risks (RR) and 95% confidence intervals (95% CI). During up to 8.2 years of follow-up, 17,872 prostate cancer cases were identified, including 1,942 advanced and 513 fatal cases. Comparing frequent (5+ times per week) versus infrequent (less than once per week) vigorous exercise, exercise at baseline was not associated with risk of total prostate cancer (RR, 1.01; 95% CI, 0.96-1.07; P(trend) = 0.78), advanced prostate cancer (RR, 1.14; 95% CI, 0.97-1.33; P(trend) = 0.25), or fatal prostate cancer (RR, 0.90; 95% CI, 0.67-1.20; P(trend) = 0.12). Increasing level of vigorous exercise during adolescence was associated with a small 3% reduction in total prostate cancer risk (frequent versus infrequent exercise during adolescence: RR, 0.97; 95% CI, 0.91-1.03; P(trend) = 0.03) but was not associated with risk of advanced prostate cancer (RR, 0.95; 95% CI, 0.78-1.14; P(trend) = 0.18) or fatal prostate cancer (RR, 0.96; 95% CI, 0.67-1.36; P(trend) = 0.99). Neither vigorous exercise at baseline nor exercise during adolescence was related to risk of total, advanced, or fatal prostate cancer in this large prospective cohort.

Physical activity and mammographic breast density in the EPIC-Norfolk cohort study.

Physical inactivity and high mammographic breast density have both been associated with increased breast cancer risk. However, the association between physical activity and mammographic breast density remains inconsistent. In the European Prospective Investigation of Cancer (EPIC)-Norfolk population-based cohort study (United Kingdom), the authors investigated the cross-sectional association between physical activity level at baseline during 1993-1997 and breast density among 1,394 postmenopausal, cancer-free women. Usual physical activity was assessed by a brief, validated questionnaire. Percentage breast density was determined visually from mammograms by three trained radiologists using the Boyd six-category scale. The association between physical activity level and breast density risk category was examined. No statistically significant association between physical activity and percentage breast density was observed in the unadjusted or adjusted regression models. A suggested increase in breast density for the most active women in the unadjusted regression analysis (odds ratio = 1.13, 95% confidence interval: 0.71, 1.80) was reversed after inclusion of body mass index and reproductive and lifestyle variables (odds ratio = 0.78, 95% confidence interval: 0.45, 1.34). The lack of an association between physical activity and percentage breast density suggests that an association between physical activity and breast cancer risk is unlikely to be mediated through an effect on mammographic breast density.