Wearable Devices in Cardiovascular Medicine.

Wearable devices, such as smartwatches and activity trackers, are commonly used by patients in their everyday lives to manage their health and well-being. These devices collect and analyze long-term continuous data on measures of behavioral or physiologic function, which may provide clinicians with a more comprehensive view of a patients' health compared with the traditional sporadic measures captured by office visits and hospitalizations. Wearable devices have a wide range of potential clinical applications ranging from arrhythmia screening of high-risk individuals to remote management of chronic conditions such as heart failure or peripheral artery disease. As the use of wearable devices continues to grow, we must adopt a multifaceted approach with collaboration among all key stakeholders to effectively and safely integrate these technologies into routine clinical practice. In this Review, we summarize the features of wearable devices and associated machine learning techniques. We describe key research studies that illustrate the role of wearable devices in the screening and management of cardiovascular conditions and identify directions for future research. Last, we highlight the challenges that are currently hindering the widespread use of wearable devices in cardiovascular medicine and provide short- and long-term solutions to promote increased use of wearable devices in clinical care.

Plasma metabolomic signature of healthy lifestyle, structural brain reserve and risk of dementia.

Although the association between healthy lifestyle and dementia risk has been documented, the relationship between a metabolic signature indicative of healthy lifestyle and dementia risk and the mediating role of structural brain impairment remain unknown. We retrieved 136 628 dementia-free participants from UK Biobank. Elastic net regression was used to obtain a metabolic signature that represented lifestyle behaviours. Cox proportional hazard models were fitted to explore the associations of lifestyle-associated metabolic signature with incident dementia. Causal associations between identified metabolites and dementia were investigated using Mendelian randomization. Mediation analysis was also conducted to uncover the potential mechanisms involving 19 imaging-derived phenotypes (brain volume, grey matter volume, white matter volume and regional grey matter volumes). During a follow-up of 12.55 years, 1783 incident cases of all-cause dementia were identified, including 725 cases of Alzheimer's dementia and 418 cases of vascular dementia. We identified 83 metabolites that could represent healthy lifestyle behaviours using elastic net regression. The metabolic signature was associated with a lower dementia risk, and for each standard deviation increment in metabolic signature, the hazard ratio was 0.89 [95% confidence interval (CI): 0.85, 0.93] for all-cause dementia, 0.95 (95% CI: 0.88, 1.03) for Alzheimer's dementia and 0.84 (95% CI: 0.77, 0.91) for vascular dementia. Mendelian randomization revealed potential causal associations between the identified metabolites and risk of dementia. In addition, the specific structural brain reserve, including the hippocampus, grey matter in the hippocampus, parahippocampal gyrus and middle temporal gyrus, were detected to mediate the effects of metabolic signature on dementia risk (mediated proportion ranging from 6.21% to 11.98%). The metabolic signature associated with a healthy lifestyle is inversely associated with dementia risk, and greater structural brain reserve plays an important role in mediating this relationship. These findings have significant implications for understanding the intricate connections between lifestyle, metabolism and brain health.

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Healthy lifestyle and cancer survival: A multinational cohort study.

Lifestyle factors after a cancer diagnosis could influence the survival of cancer 60 survivors. To examine the independent and joint associations of healthy lifestyle factors with mortality outcomes among cancer survivors, four prospective cohorts (National Health and Nutrition Examination Survey [NHANES], National Health Interview Survey [NHIS], UK Biobank [UKB] and Kailuan study) across three countries. A healthy lifestyle score (HLS) was defined based on five common lifestyle factors (smoking, alcohol drinking, diet, physical activity and body mass index) that related to cancer survival. We used Cox proportional hazards regression to estimate the hazard ratios (HRs) for the associations of individual lifestyle factors and HLS with all-cause and cancer mortality among cancer survivors. During the follow-up period of 37,095 cancer survivors, 8927 all-cause mortality events were accrued in four cohorts and 4449 cancer death events were documented in the UK and US cohorts. Never smoking (adjusted HR = 0.77, 95% CI: 0.69-0.86), light alcohol consumption (adjusted HR = 0.86, 95% CI: 0.82-0.90), adequate physical activity (adjusted HR = 0.90, 95% CI: 0.85-0.94), a healthy diet (adjusted HR = 0.69, 95% CI: 0.61-0.78) and optimal BMI (adjusted HR = 0.89, 95% CI: 0.85-0.93) were significantly associated with a lower risk of all-cause mortality. In the joint analyses of HLS, the HR of all-cause and cancer mortality for cancer survivors with a favorable HLS (4 and 5 healthy lifestyle factors) were 0.55 (95% CI 0.42-0.64) and 0.57 (95% CI 0.44-0.72), respectively. This multicohort study of cancer survivors from the United States, the United Kingdom and China found that greater adherence to a healthy lifestyle might be beneficial in improving cancer prognosis.

Healthy dietary patterns and risk of prostate cancer in men at high genetic risk.

Prostate cancer has high heritability. Healthy lifestyle has been associated with lower lethal prostate cancer risk among men at increased genetic susceptibility, but the role of healthy dietary patterns remains unknown. We prospectively followed 10,269 genotyped men in the Health Professionals Follow-up Study (1993-2019). Genetic risk was quantified using an established polygenic risk score (PRS). Five dietary patterns were investigated: healthy eating index, Mediterranean, diabetes risk-reducing, hyperinsulinemic and inflammatory diet. Overall and lethal prostate cancer rates (metastatic disease/prostate cancer-specific death) were analyzed using multivariable Cox proportional hazards models. During 26 years of follow-up, 2133 overall and 253 lethal prostate cancer events were documented. In the highest PRS quartile, higher adherence to a diabetes risk-reducing diet was associated with lower rates of overall (top vs. bottom quintile HR [95% CI], 0.74 [0.58-0.94]) and lethal prostate cancer (0.43 [0.21-0.88]). A low insulinemic diet was associated with similar lower rates (overall, 0.76 [0.60-0.95]; lethal, 0.46 [0.23-0.94]). Other dietary patterns showed weaker, but similar associations. In the highest PRS quartile, men with healthy lifestyles based on body weight, physical activity, and low insulinemic diet had a substantially lower rate (0.26 [0.13-0.49]) of lethal prostate cancer compared with men with unhealthy lifestyles, translating to a lifetime risk of 3.4% (95% CI, 2.3%-5.0%) among those with healthy lifestyles and 9.5% (5.3%-16.7%) among those with unhealthy lifestyles. Our findings indicate that lifestyle modifications lowering insulin resistance and chronic hyperinsulinemia could be relevant in preventing aggressive prostate cancer among men genetically predisposed to prostate cancer.

Healthy lifestyle and prostate cancer risk in the Million Veteran Program.

BACKGROUND: This study aims to assess the impact of healthy lifestyle on prostate cancer (PCa) risk in a diverse population. METHODS: Data for 281,923 men from the Million Veteran Program (MVP), a nationwide, health system-based cohort study, were analyzed. Self-reported information at enrollment included smoking status, exercise, diet, family history of PCa, and race/ethnicity. Body mass index (BMI) was obtained from clinical records. Genetic risk was assessed via a validated polygenic score. Cox proportional hazards models were used to assess associations with PCa outcomes. RESULTS: After accounting for ancestry, family history, and genetic risk, smoking was associated with an increased risk of metastatic PCa (hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.64-2.02; p < 10-16) and fatal PCa (HR, 2.73; 95% CI, 2.36-3.25; p < 10-16). Exercise was associated with a reduced risk of fatal PCa (HR, 0.86; 95% CI, 0.76-0.98; p = .03). Higher BMI was associated with a slightly reduced risk of fatal PCa, and diet score was not independently associated with any end point. Association with exercise was strongest among those who had nonmetastatic PCa at MVP enrollment. Absolute reductions in the risk of fatal PCa via lifestyle factors were greatest among men of African ancestry (1.7% for nonsmokers vs. 6.1% for smokers) or high genetic risk (1.4% for nonsmokers vs. 4.3% for smokers). CONCLUSIONS: Healthy lifestyle is minimally related to the overall risk of developing PCa but is associated with a substantially reduced risk of dying from PCa. In multivariable analyses, both exercise and not smoking remain independently associated with reduced metastatic and fatal PCa.

The role of lifestyle in the association between long-term ambient air pollution exposure and cardiovascular disease: a national cohort study in China.

BACKGROUND: Cardiovascular disease (CVD) caused by air pollution poses a considerable burden on public health. We aim to examine whether lifestyle factors mediate the associations of air pollutant exposure with the risk of CVD and the extent of the interaction between lifestyles and air pollutant exposure regarding CVD outcomes. METHODS: We included 7000 participants in 2011-2012 and followed up until 2018. The lifestyle evaluation consists of six factors as proxies, including blood pressure, blood glucose, blood lipids, body mass index, tobacco exposure, and physical activity, and the participants were categorized into three lifestyle groups according to the number of ideal factors (unfavorable, 0-1; intermediate, 2-4; and favorable, 5-6). Satellite-based spatiotemporal models were used to estimate exposure to ambient air pollutants (including particles with diameters ≤ 1.0 µm [PM1], ≤ 2.5 µm [PM2.5], ≤ 10 µm [PM10], nitrogen dioxide [NO2], and ozone [O3]). Cox regression models were used to examine the associations between air pollutant exposure, lifestyles and the risk of CVD. The mediation and modification effects of lifestyle categories on the association between air pollutant exposure and CVD were analyzed. RESULTS: After adjusting for covariates, per 10 µg/m3 increase in exposure to PM1 (HR: 1.09, 95% CI: 1.05-1.14), PM2.5 (HR: 1.04, 95% CI: 1.00-1.08), PM10 (HR: 1.05, 95% CI: 1.03-1.08), and NO2 (HR: 1.11, 95% CI: 1.05-1.18) was associated with an increased risk of CVD. Adherence to a healthy lifestyle was associated with a reduced risk of CVD compared to an unfavorable lifestyle (HR: 0.65, 95% CI: 0.56-0.76 for intermediate lifestyle and HR: 0.41, 95% CI: 0.32-0.53 for favorable lifestyle). Lifestyle played a significant partial mediating role in the contribution of air pollutant exposure to CVD, with the mediation proportion ranging from 7.4% for PM10 to 14.3% for PM2.5. Compared to an unfavorable lifestyle, the relative excess risk due to interaction for a healthier lifestyle to reduce the effect on CVD risk was - 0.98 (- 1.52 to - 0.44) for PM1, - 0.60 (- 1.05 to - 0.14) for PM2.5, - 1.84 (- 2.59 to - 1.09) for PM10, - 1.44 (- 2.10 to - 0.79) for NO2, and - 0.60 (- 1.08, - 0.12) for O3. CONCLUSIONS: Lifestyle partially mediated the association of air pollution with CVD, and adherence to a healthy lifestyle could protect middle-aged and elderly people from the adverse effects of air pollution regarding CVD.

Healthy lifestyle change and all-cause and cancer mortality in the European Prospective Investigation into Cancer and Nutrition cohort.

BACKGROUND: Healthy lifestyles are inversely associated with the risk of noncommunicable diseases, which are leading causes of death. However, few studies have used longitudinal data to assess the impact of changing lifestyle behaviours on all-cause and cancer mortality. METHODS: Within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, lifestyle profiles of 308,497 cancer-free adults (71% female) aged 35-70 years at recruitment across nine countries were assessed with baseline and follow-up questionnaires administered on average of 7 years apart. A healthy lifestyle index (HLI), assessed at two time points, combined information on smoking status, alcohol intake, body mass index, and physical activity, and ranged from 0 to 16 units. A change score was calculated as the difference between HLI at baseline and follow-up. Associations between HLI change and all-cause and cancer mortality were modelled with Cox regression, and the impact of changing HLI on accelerating mortality rate was estimated by rate advancement periods (RAP, in years). RESULTS: After the follow-up questionnaire, participants were followed for an average of 9.9 years, with 21,696 deaths (8407 cancer deaths) documented. Compared to participants whose HLIs remained stable (within one unit), improving HLI by more than one unit was inversely associated with all-cause and cancer mortality (hazard ratio [HR]: 0.84; 95% confidence interval [CI]: 0.81, 0.88; and HR: 0.87; 95% CI: 0.82, 0.92; respectively), while worsening HLI by more than one unit was associated with an increase in mortality (all-cause mortality HR: 1.26; 95% CI: 1.20, 1.33; cancer mortality HR: 1.19; 95% CI: 1.09, 1.29). Participants who worsened HLI by more than one advanced their risk of death by 1.62 (1.44, 1.96) years, while participants who improved HLI by the same amount delayed their risk of death by 1.19 (0.65, 2.32) years, compared to those with stable HLI. CONCLUSIONS: Making healthier lifestyle changes during adulthood was inversely associated with all-cause and cancer mortality and delayed risk of death. Conversely, making unhealthier lifestyle changes was positively associated with mortality and an accelerated risk of death.

Association of combined healthy lifestyle factors with incident osteoporosis in patients with and without type 2 diabetes.

PURPOSE: The association between type 2 diabetes mellitus (T2DM), lifestyle factors, and the risk of osteoporosis (OP) is well-established. However, the impact of a healthy lifestyle on diabetes-related osteoporosis needs further investigation. Our objective was to explore if a combination of healthy lifestyle factors could mitigate the risk of OP in individuals with type 2 diabetes. METHODS: This longitudinal analysis included 237,725 middle-aged and older participants. An overall lifestyle score, ranging from 0 to 7, was calculated by assigning a point for each of the seven healthy lifestyle factors, including no current smoking, non-excessive alcohol consumption, regular physical activity, healthy diet, adequate sleep duration, less sedentary behavior, and adequate sunshine exposure. RESULTS: During a median follow-up 12.21 years, 5760 OP cases were documented. Participants with T2DM showed a higher risk of OP than those without diabetes. Compared with participants without diabetes who had a lifestyle score of 6-7, the hazard ratios (HRs) for OP were 1.58 (95% CI 1.23-2.03), 1.62 (95% CI 1.16-2.25), and 2.58 (95% CI 1.64-4.05) for participants with T2DM who had a lifestyle score of 4, 3, and 0-2, respectively. There was a graded association between higher lifestyle scores and lower risks of incident OP among participants without diabetes as well as among those with T2DM. We estimated that the population attributable fraction for not adhering to 6-7 lifestyle behaviors was 15.7%. CONCLUSIONS: Participants with T2DM who adhered to a variety of healthy lifestyle factors demonstrated a substantially reduced risk of developing OP.

Association of combined healthy lifestyle with risk of adverse outcomes in patients with prediabetes.

OBJECTIVE: Prediabetes and lifestyle factors have been associated with the risks of multiple adverse outcomes, but the effect of a healthy lifestyle on prediabetes-related complications remains unknown. We aimed to investigate whether the risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and chronic kidney disease (CKD) among individuals with prediabetes can be offset by a broad combination of healthy lifestyle factors. METHODS: This prospective study used data from the UK Biobank cohort. An overall lifestyle score ranging from 0 to 6 was created with 1 point for each of the 6 healthy lifestyle factors: no current smoking, moderate alcohol consumption, regular physical activity, healthy diet, no overweight or obese, and adequate sleep duration. T2DM, CVD, and CKD were ascertained during a median follow-up of 14 years. Cox proportional hazard regression models were used to estimate the associations. Sensitivity analyses were performed to test the robustness of the results. RESULTS: We included 202,993 participants without T2DM, CVD, and CKD at baseline (mean age 55.5 years [SD 8.1]; 54.7% were women). Among these participants, 6,745, 16,961, and 6,260 participants eventually developed T2DM, CVD, and CKD, respectively. Compared with the participants with normoglycaemia, those with prediabetes showed a higher risk of these adverse outcomes. In addition, those prediabetic participants with a lifestyle score of 0-1 had a significantly higher risk of T2DM (hazard ratio [HR] 16.73, 95% CI 14.24, 19.65), CVD (HR 1.96, 95% CI 1.74, 2.21), and CKD (HR 1.92, 95% CI 1.58, 2.34) compared with those with no prediabetes and a score of 5-6. Moreover, among the participants with prediabetes, the HRs for T2DM, CVD, and CKD comparing a lifestyle score of 5-6 versus 0-1 decreased to 0.43 (95% CI 0.36, 0.51), 0.52 (95% CI 0.44, 0.62), and 0.60 (95% CI 0.46, 0.79), respectively. CONCLUSIONS: Combined healthy lifestyle factors were associated with a significantly lower risk of multiple adverse outcomes, including T2DM, CVD, and CKD. This indicates that prioritising multifactorial approaches to behavioural lifestyle modification is crucial for preventing and postponing the development of complications related to prediabetes.

Association of lifestyle with reduced stroke risk in 41 314 individuals with diabetes: Two prospective cohort studies in China.

AIM: To investigate the associations of individual and combined healthy lifestyle factors (HLS) with the risk of stroke in individuals with diabetes in China. METHODS: This prospective analysis included 41 314 individuals with diabetes [15 191 from the Comprehensive Research on the Prevention and Control of the Diabetes (CRPCD) project and 26 123 from the China Kadoorie Biobank (CKB) study]. Associations of lifestyle factors, including cigarette smoking, alcohol consumption, physical activity, diet, body shape and sleep duration, with the risk of stroke, intracerebral haemorrhage (ICH) and ischaemic stroke (IS) were assessed using Cox proportional hazard models. RESULTS: During median follow-up periods of 8.02 and 9.05 years, 2499 and 4578 cases of stroke, 2147 and 4024 of IS, and 160 and 728 of ICH were documented in individuals with diabetes in the CRPCD and CKB cohorts, respectively. In the CRPCD cohort, patients with ≥5 HLS had a 14% lower risk of stroke (hazard ratio (HR): 0.86, 95% confidence interval (CI): 0.75-0.98) than those with ≤2 HLS. In the CKB cohort, the adjusted HR (95% CI) for patients with ≥5 HLS were 0.74 (0.66-0.83) for stroke, 0.74 (0.66-0.83) for IS, and 0.57 (0.42-0.78) for ICH compared with those with ≤2 HLS. The pooled adjusted HR (95% CI) comparing patients with ≥5 HLS versus ≤2 HLS was 0.79 (0.69-0.92) for stroke, 0.80 (0.68-0.93) for IS, and 0.60 (0.46-0.78) for ICH. CONCLUSIONS: Maintaining a healthy lifestyle was associated with a lower risk of stroke, IS and ICH among individuals with diabetes.

Baseline engagement with healthy lifestyles and their associations with health outcomes in people with multiple sclerosis enrolled in an online multimodal lifestyle course.

BACKGROUND AND PURPOSE: Healthy lifestyle behaviour modification may improve health outcomes in people with multiple sclerosis (pwMS), but empirical evidence is needed to confirm prior study findings. We developed an online multimodal lifestyle intervention (Multiple Sclerosis Online Course) to examine the impact of lifestyle modification on health outcomes in pwMS via a randomized control trial (RCT). However, the present study specifically analyses baseline data to assess engagement with healthy lifestyles by RCT participants and cross-sectional associations with health outcomes. METHODS: Baseline engagement with six "healthy lifestyle behaviours" of the intervention course (high-quality, plant-based diet; ≥5000 IU/day vitamin D; omega-3 supplementation; ≥30 min physical activity 5 times/week; ≥30 min/week meditation; and nonsmoking) was examined. Associations between individual versus collective behaviours (individual behaviours summated) and health outcomes (quality of life [QoL]/fatigue/disability) were evaluated using multivariate modelling (linear/log-binomial/multinomial). RESULTS: At baseline, 33.7% and 30.0% of participants (n = 857) engaged in one or two healthy behaviours, respectively. In total, engagement with healthy lifestyles by participants was as follows: nonsmoking, 90.7%; omega-3 supplementation, 34.5%; vitamin D supplementation, 29.8%; physical activity, 29.4%; diet, 10.7%; and meditation, 10.5%. Individual behaviours (nonsmoking/physical activity/diet) were independently associated with better health outcomes. Engagement with multiple behaviours, especially diet and physical activity, was associated with better outcomes; engaging with ≥4 behaviours was associated with a 9.0-point higher mental QoL and a 9.5-point higher physical QoL, as well as 23% and 56% lower prevalence of fatigue and moderate disability, respectively. CONCLUSIONS: Baseline engagement with ≥4 healthy behaviours, including diet and physical activity, was associated with better health outcomes.

Towards new perspectives: A scoping review and meta-synthesis to redefine brain health for multiple sclerosis.

BACKGROUND AND PURPOSE: Research promoting the health of the brain has increased exponentially over the last decade. The importance of 'brain health' for multiple sclerosis (MS), as one example, is a high priority. However, as research into the concept increases, so does varied use of the term. METHODS: A scoping review, guided by the methodological framework of the Joanna Briggs Institute, was conducted to collate the evidence relating to brain health for MS. A comprehensive literature search incorporated six search strategies to retrieve both scientific and grey literature sources. All evidence sources were qualitatively charted and synthesized (meta-synthesis) according to their definition of brain health used, outcome measures and brain-healthy lifestyle elements. RESULTS: Seventy evidence sources (34 peer reviewed, 36 grey literature) were eligible for inclusion. Of these, just over half (n = 40, 57%) provided a definition of brain health. The most common definition alluded to the biomedical model of neurological reserve (n = 22, 55%), a self-remodelling theory described to retain optimal brain function. Twenty-nine outcome measures of brain health were identified, the most frequent being magnetic resonance imaging metrics (n = 25, 83%). Physical activity was the most prevalent brain-healthy lifestyle element (n = 44), followed by avoidance of smoking (n = 26) and diet (n = 24). CONCLUSIONS: Brain health should be considered a primary target for optimal disease and lifestyle management across the MS disease course. A working definition reflecting a shift from a medical lens towards broader biopsychosocial contexts that may influence brain health for people living with MS is proposed.

Development and validation of a lifestyle risk index to screen for metabolic syndrome and its components in two multi-ethnic cohorts.

BACKGROUND: Metabolic syndrome (MetS) is a precursor to cardiovascular diseases and type 2 diabetes. Existing MetS prediction models relied heavily on biochemical measures and those based on non-invasive predictors such as lifestyle behaviours were limited. We aim to (1) develop a weighted lifestyle risk index for MetS and (2) externally validate this index using two Asian-based cohorts in Singapore. METHODS: Using data from the Multi-Ethnic Cohort (MEC) 1 (n = 2873, 41% male), multiple logistic regression was used to identify predictors associated with MetS. A weighted lifestyle risk index was generated using coefficients of the selected predictors in the development cohort (MEC1). Subsequently, the performance of the lifestyle risk index in predicting the occurrence of MetS within 10 years was assessed by discrimination and calibration in an external validation cohort (MEC2) (n = 6070, 43% male). RESULTS: A lifestyle risk index for MetS with nine predictors was developed (age, sex, ethnicity, having a family history of diabetes, BMI, diet, physical activity, smoking status, and screen time). This index demonstrated acceptable discrimination in the development cohort [AUC (95% CI) = 0.74 (0.71, 0.76)] and the validation cohort [AUC (95% CI) = 0.79 (0.77, 0.81)]. CONCLUSION: This lifestyle risk index exhibits potential for risk stratification in population-based screening programmes. Future research could apply a similar methodology to develop disease-specific lifestyle risk indices using nationwide registry-based data.

Genetic risk, adherence to healthy lifestyle behaviors, and risk of cholelithiasis: A population-based cohort study.

OBJECTIVE: Genetic and lifestyles contribute to cholelithiasis, but the impact of adhering to healthy lifestyle on cholelithiasis risk remains uncertain. We aimed to assess combined lifestyle factors and a polygenic risk score on incident cholelithiasis. METHODS: We utilized cholelithiasis genome-wide association study (GWAS) data from FinnGen study, constructing varied polygenic risk score (PRS), and applied them to 317,640 UK Biobank participants. The relative and absolute risk of incident cholelithiasis associated with six well-established lifestyle risk factors, was evaluated and stratified by PRS (low risk [quintile 1], intermediate risk [quintiles 2-4] and high risk [quintile 5]). Lifestyle score was also categorized into favorable, intermediate, and unfavorable groups. RESULTS: The PRS derived from 13 single nucleotide polymorphisms (p ≤ 5 × 10-6, r2 < 0.001) showed the best performance. A significant gradient of increase in risk of cholelithiasis was observed across the quintiles of the polygenic risk score (p < 0.001). Compared to participants with low genetic risk, those with intermediate or high genetic risk had a 10% (95% confidence interval [CI] = 1.05-1.17) and 24% (95% CI = 1.16-1.32) higher risk of cholelithiasis. An unfavorable lifestyle was associated with an approximately 50% higher risk of cholelithiasis than a favorable lifestyle. Participants with high genetic risk and an unfavorable lifestyle had 98% (Hazard ratio [HR]: 1.98; 95% CI: 1.67-2.35) higher risk of cholelithiasis than those with low genetic risk and a favorable lifestyle. CONCLUSIONS: Our study highlights the importance of lifestyle behaviors intervention on cholelithiasis risk regardless of the genetic risk in White European population.

Implementation of recommended treatment for children in weight management programs: Lessons from the stay in treatment study sites.

OBJECTIVES: Pediatric obesity remains a public health crisis in the United States, exacerbated by the COVID-19 pandemic. There are recommended guidelines for multidisciplinary care, but they remain challenging to implement, even in tertiary care weight management programs. The aim of this analysis is to describe the implementation of these recommendations among four pediatric weight management programs in the United States. METHODS: This report capitalizes on a convenience sample of programs participating in the Stay In Treatment (SIT) Study, a multicenter study to address attrition among pediatric weight management programs in tertiary care, academic institutions in diverse geographic locations. The programs were compared regarding structure, program offerings, and funding support. RESULTS: The four programs were interdisciplinary, offered individual and group treatment options, and were family-based. A range of clinicians provided interventions with nutrition, physical activity, behavioral and psychosocial components. Anti-obesity pharmacotherapy and bariatric surgery were offered, when appropriate. None of the programs were self-sustaining; they required institutional and philanthropic support to provide recommended, comprehensive treatment. CONCLUSIONS: Ongoing state and national advocacy are needed in the US to create consistent coverage for private and public insurance plans, so that high-risk children can have access to recommended treatment.

Healthy Lifestyles Modify the Association of Melatonin Receptor 1B Gene and Ischemic Stroke: A Family-Based Cohort Study in Northern China.

Limited research has reported the association between MTNR1B gene polymorphisms and ischemic stroke (IS), and there is insufficient evidence on whether adopting a healthy lifestyle can mitigate genetic risks in this context. This study aimed to investigate the associations between MTNR1B gene variants (rs10830963 and rs1387153) and IS, examining the potential effect of gene-lifestyle interactions on IS risk. Conducted in northern China, this family-based cohort study involved 5116 initially IS-free subjects. Genotype data for rs10830963 and rs1387153 in MTNR1B were collected. Eight modifiable lifestyle factors, including body mass index (BMI), smoking, alcohol consumption, dietary habits, physical activity, sedentary time, sleep duration, and chronotype, were considered in calculating healthy lifestyle scores. Multilevel Cox models were used to examine the associations between MTNR1B variants and IS. Participants carrying the rs10830963-G and rs1387153-T alleles exhibited an elevated IS risk. Each additional rs10830963-G allele and rs1387153-T allele increased the IS risk by 36% (HR = 1.36, 95% CI, 1.12-1.65) and 32% (HR = 1.32, 95% CI, 1.09-1.60), respectively. Participants were stratified into low, medium, and high healthy lifestyle score groups (1537, 2188, and 1391 participants, respectively). Genetic-lifestyle interactions were observed for rs10830963 and rs1387153 (p for interaction < 0.001). Notably, as the healthy lifestyle score increased, the effect of MTNR1B gene variants on IS risk diminished (p for trend < 0.001). This study underscores the association between the MTNR1B gene and IS, emphasizing that adherence to a healthy lifestyle can mitigate the genetic predisposition to IS.

Sleep and periodontal health.

Sleep is fundamental for health and well-being. An adequate amount and quality of sleep is a cardinal component of a healthy lifestyle at the basis of the prevention of many non-communicable chronic diseases. Recent evidence suggests that sleep disorders, particularly obstructive sleep apnea, represent an emerging risk factor for periodontal health. This review article provides a critical appraisal of the existing literature concerning the association between sleep duration, sleep quality, sleep disorders in general, and obstructive sleep apnea with periodontal diseases, including gingivitis and periodontitis. The putative mechanisms underlying these associations are described as well as the potential clinical implications for diagnosis and treatment.

Lifestyle changes in middle age and risk of cancer: evidence from the European Prospective Investigation into Cancer and Nutrition.

In this study, we aimed to provide novel evidence on the impact of changing lifestyle habits on cancer risk. In the EPIC cohort, 295,865 middle-aged participants returned a lifestyle questionnaire at baseline and during follow-up. At both timepoints, we calculated a healthy lifestyle index (HLI) score based on cigarette smoking, alcohol consumption, body mass index and physical activity. HLI ranged from 0 (most unfavourable) to 16 (most favourable). We estimated the association between HLI change and risk of lifestyle-related cancers-including cancer of the breast, lung, colorectum, stomach, liver, cervix, oesophagus, bladder, and others-using Cox regression models. We reported hazard ratios (HR) with 95% confidence intervals (CI). Median time between the two questionnaires was 5.7 years, median age at follow-up questionnaire was 59 years. After the follow-up questionnaire, we observed 14,933 lifestyle-related cancers over a median follow-up of 7.8 years. Each unit increase in the HLI score was associated with 4% lower risk of lifestyle-related cancers (HR 0.96; 95%CI 0.95-0.97). Among participants in the top HLI third at baseline (HLI > 11), those in the bottom third at follow-up (HLI ≤ 9) had 21% higher risk of lifestyle-related cancers (HR 1.21; 95%CI 1.07-1.37) than those remaining in the top third. Among participants in the bottom HLI third at baseline, those in the top third at follow-up had 25% lower risk of lifestyle-related cancers (HR 0.75; 95%CI 0.65-0.86) than those remaining in the bottom third. These results indicate that lifestyle changes in middle age may have a significant impact on cancer risk.

Age and healthy lifestyle behavior's disparities and similarities on knowledge of myocardial infarction symptoms and risk factors among public and outpatients in a resource-limited setting, cross-sectional study in greater Gaborone, Botswana.

OBJECTIVES: In this cross-sectional study from Botswana, we investigated knowledge of myocardial infarction (MI) symptoms and risk factors among the general public and outpatients with MI risk factors based on age and lifestyle behaviors, in addition to assessing associations with sociodemographic and MI risk factors. METHOD: Open-ended questionnaires about 8 MI symptoms and 10 risk factors, were administered by research assistants to a representative selection of outpatients (n = 525) and the public (n = 2248). Weight and height were measured in all participants and BMI was calculated. Knowledge scores were compared between the two groups. We examined whether sociodemographic and MI risk factors had impact on the scores. Analyses were further adjusted for lifestyle behavior (smoking status, dietary status and physical activities). RESULTS: The valid response rate was 97.9% comprising 97.8% for the public (n = 2199) and 98.1% for outpatients (n = 515). Public respondents (35.2 ± 12.3 years) were younger than outpatients (38.5 ± 12.6 years). The public comprised 56.9% females while outpatients 54.6%. In general, outpatients had higher knowledge of MI symptoms than the public, with mean scores ± SD of 3.49 ± 2.84 vs 2.80 ± 2.54. Outpatients also had higher knowledge score of MI risk factors than the public, with mean scores, 5.33 ± 3.22 vs 3.82 ± 3.07. For MI symptoms, outpatients were more aware than the public for chest pains among all ages, for arm pain/ numbness, neck/ jaw pain radiating to/ from chest, and feeling sick or looking pallor on the skin among those aged 35-49 years. Among both the public and outpatients, lower knowledge of both MI symptoms and risk factors was associated with primary education, not residing/working together, history of hypertension, no history of heart disease/stroke, and obesity. There were similarities and disparities on MI knowledge among respondents with various numbers of healthy lifestyle behaviours. CONCLUSION: Results call for urgent educational campaigns on awareness and knowledge of MI and using strategies based on age and lifestyle behavior.