UNASSIGNED: To prospectively assess the individual and joint effects of birth weight and the life\'s essential 8 (LE8)-defined cardiovascular health (CVH) on myocardial infarction (MI) risk in later life.
UNASSIGNED: In 144,803 baseline MI-free participants who were recruited in the UK Biobank cohort between 2006 and 2010, Cox proportional hazard models were used to estimate the associations of birth weight, LE8 score, and their interactions with incident MI. LE8 was defined on the basis of diet, physical activity, nicotine exposure, sleep health, body mass index, blood pressure, blood glucose, and blood lipids.
UNASSIGNED: Low birth weight was associated with higher risk of MI [hazard ratio (HR) 1.17, 95% confidence interval 1.02-1.35, P = 0.025], while no significant correlation between high birth weight and MI was observed after adjustment. Low CVH was associated with higher MI risk [HR 6.43 (3.71-11.15), P < 0.001). Participants with low birth weight and low CVH (vs. participants with normal birth weight and high CVH) had HR of 5.97 (2.94-12.14) for MI incidence. The relative excess risk due to interaction of low birth weight and low CVH on MI was -4.11 (-8.12, -0.11), indicating a negative interaction on an additive scale. A consistent decreasing trend of MI risk along with increased LE8 score was observed across all three birth weight groups.
UNASSIGNED: Low birth weight was associated with increased MI risk, emphasizing the importance of the prenatal factor in risk prediction and prevention of MI. Improving LE8 can mitigate MI risk attributed to low birth weight.

BACKGROUND: Both body mass index (BMI) and genetic factors independently contribute to cardiovascular disease (CVD). However, it is unclear whether genetic risk modifies the association between BMI and the risk of incident CVD. This study aimed to investigate whether BMI categories and genetic risk jointly and interactively contribute to incident CVD events, including hypertension (HTN), atrial fibrillation (AF), coronary heart disease (CHD), stroke, and heart failure (HF).
METHODS: A total of 496,851 participants from the UK Biobank with one or more new-onset CVD events were included in the analyses. BMI was categorized as normal weight (< 25.0 kg/m2), overweight (25.0-29.9 kg/m2), and obesity (≥ 30.0 kg/m2). Genetic risk for each outcome was defined as low (lowest tertile), intermediate (second tertile), and high (highest tertile) using polygenic risk score. The joint associations of BMI categories and genetic risk with incident CVD were investigated using Cox proportional hazard models. Additionally, additive interactions were evaluated.
RESULTS: Among the 496,851 participants, 270,726 (54.5%) were female, with a mean (SD) age was 56.5 (8.1) years. Over a median follow-up (IQR) of 12.4 (11.5-13.1) years, 102,131 (22.9%) participants developed HTN, 26,301 (5.4%) developed AF, 32,222 (6.9%) developed CHD, 10,684 (2.2%) developed stroke, and 13,304 (2.7%) developed HF. Compared with the normal weight with low genetic risk, the obesity with high genetic risk had the highest risk of CVD: HTN (HR: 3.96; 95%CI: 3.84-4.09), AF (HR: 3.60; 95%CI: 3.38-3.83), CHD (HR: 2.76; 95%CI: 2.61-2.91), stroke (HR: 1.44; 95%CI: 1.31-1.57), and HF (HR: 2.47; 95%CI: 2.27-2.69). There were significant additive interactions between BMI categories and genetic risk for HTN, AF, and CHD, with relative excess risk of 0.53 (95%CI: 0.43-0.62), 0.67 (95%CI: 0.51-0.83), and 0.37 (95%CI: 0.25-0.49), respectively.
CONCLUSIONS: BMI and genetic factors jointly and interactively contribute to incident CVD, especially among participants with high genetic risk. These findings have public health implications for identifying populations more likely to have cardiovascular benefit from weight loss interventions.

UNASSIGNED: Previous research on the association between serum albumin (ALB) and venous thromboembolism (VTE) has produced inconclusive results. The polygenic risk score is constructed from a set of independent risk variants associated with a disorder, enabling the identification of a larger fraction of the population at comparable or greater disease risk. It is still unknown whether ALB and genetic factors jointly contribute to the incidence of VTE.
UNASSIGNED: The present study aimed to explore ALB, genetic susceptibility, and the risk of VTE.
UNASSIGNED: The present investigation was an analysis of prospectively collected data from UK Biobank, a population-based, longitudinal cohort. Cox proportional models were used to calculate hazard ratios and 95% CIs for VTE. The Kaplan-Meier curve was utilized to visualize the cumulative risk of VTE according to different serum ALB levels, and the restricted cubic spline model was leveraged to explore the exposure-response relationship among ALB levels and VTE risk.
UNASSIGNED: During median follow-up of 13.5 years, 11,502 cases with VTE were diagnosed among 417,113 participants in the UK Biobank. The lower ALB levels were associated with a higher risk for VTE. Individuals with both a high genetic risk and lowest ALB level had the highest risk of VTE (hazard ratio, 3.89; 95% CI, 3.41-4.43), compared with those with low genetic risk and highest ALB level. The positive joint effects of low ALB and polygenic risk score increased the risk of VTE in individuals with high genetic risk. This study excluded non-European patients and primarily focused on the European population, which may limit the generalizability of the findings.
UNASSIGNED: Low serum ALB levels were linked to an increased risk of VTE, which was in accordance with a linear dose-response relationship. There was a positive additive effect of ALB and genetic susceptibility on the risk of VTE. ALB could serve as a biomarker for predicting the risk of VTE.

Individual participant data (IPD) meta-analysis provides important opportunities to study interaction and effect modification for which individual studies often lack power. While previous meta-analyses have commonly focused on multiplicative interaction, additive interaction holds greater relevance for public health and may in certain contexts better reflect biological interaction. Methodological literature on interaction in IPD meta-analysis does not cover additive interaction for models including binary or time-to-event outcomes. We aimed to describe how the Relative Excess Risk due to Interaction (RERI) and other measures of additive interaction or effect modification can be validly estimated within two-stage IPD meta-analysis. First, we explain why direct pooling of study-level RERI estimates may lead to invalid results. Next, we propose a three-step procedure to estimate additive interaction: 1) estimate effects of both exposures and their product term on the outcome within each individual study; 2) pool study-specific estimates using multivariate meta-analysis; 3) estimate an overall RERI and 95% confidence interval based on the pooled effect estimates. We illustrate this procedure by investigating interaction between depression and smoking and risk of smoking-related cancers using data from the PSYchosocial factors and Cancer (PSY-CA) consortium. We discuss implications of this procedure, including the application in meta-analysis based on published data.

OBJECTIVE: Limited research has examined the combined effects of psychosocial and material adversities experienced in childhood during adolescence, a sensitive period of rapid social, emotional, and cognitive development. We investigated the interaction effect of adverse childhood experiences (ACEs) and socioeconomic circumstances (SECs) during childhood on emerging self-reported depressive symptomatology among 13-year-old Portuguese adolescents.
METHODS: We utilized data from 4,280 participants in the Generation XXI birth cohort, collected during the baseline (2005-2006), third (2016-2017), and fourth waves (2018-2020). Adjusted odds ratios and 95% confidence intervals (CIs) were obtained from binary logistic regression analyses to estimate the likelihood of depressive symptoms based on exposure to ACEs and adverse family SECs. The interaction effect of ACEs and SECs was evaluated in terms of departure from additive and multiplicative models.
RESULTS: Adolescents who reported experiences of abuse, school-related problems, and household dysfunction, as well as those from less advantaged family SECs at age 10, were more likely to report moderate to severe depressive symptoms at age 13, compared to their more affluent counterparts. We observed a significant additive interaction between low household income and abuse in the development of moderate to severe depressive symptoms [relative excess risk due to interaction, 0.69, 95% CI: 0.11, 1.26]. This interaction remained statistically significant on a multiplicative scale [OR = 1.61, 95% CI: 0.99, 2.69].
CONCLUSIONS: ACEs and low family SECs during the first decade of life are associated with an elevated risk of moderate to severe depressive symptoms in adolescence.

Background and Objectives: In the context of disease prevention, interaction on an additive scale is commonly assessed to determine synergistic effects between exposures. While the \"Relative Excess Risk due to Interaction\" represents the main measure of additive interaction between risk factors, in this study we aimed to extend this approach to assess additive interaction between factors known to prevent the event\'s occurrence, such as medical interventions and drugs. Materials and Methods: We introduced and described the \"Relative Risk Reduction due to Interaction\" (RRRI) as a key measure to assess additive interactions between preventive factors, such as therapeutic interventions and drug combinations. For RRRI values closer to 1, the combination of exposures has a greater impact on reducing the event risk due to their interaction. As a purely illustrative example, we re-evaluated a previous investigation of the synergistic effect between statins and blood pressure-lowering drugs in preventing major adverse cardiovascular events (MACE). Moreover, simulation studies were used to empirically evaluate the performance of a robust Poisson regression model to estimate RRRI across different scenarios. Results: In our example, the drug combination revealed a positive additive interaction in further reducing MACE risk (RRRI > 0), even if not statistically significant. This result is more straightforward to interpret as compared to the original one based on the RERI. Additionally, our simulations highlighted the importance of large sample sizes for detecting significant interaction effects. Conclusion: We recommend RRRI as the main measure to be considered when exploring additive interaction effects between protective exposures, such as the investigation of synergistic effects between drug combinations or preventive treatments.

OBJECTIVE: Although smoking is a recognized risk factor for chronic kidney disease (CKD), the relationship between the time smoking is initiated after awakening each day and CKD remains largely unstudied. This study examined the association between this timing and the risk of CKD, and the potential interactions of smoking timing with other risk factors for the occurrence of CKD.
METHODS: Observational cohort study.
METHODS: A total of 32,776 participants in the UK Biobank with complete data on the time from waking to the first cigarette and free of prevalent CKD were included.
METHODS: Time from waking to the first cigarette.
RESULTS: Incident CKD cases.
METHODS: Cox proportional hazards regression was used to investigate the associations between the time smoking is initiated each day and the risk of CKD. The potential interactions of smoking timing with risk factors in relationship to CKD risk were assessed on both multiplicative and additive scales.
RESULTS: During a median follow-up period of 12 years, 940 incident CKD cases occurred. Shorter durations of time from waking to the first cigarette were associated with a higher risk of incident CKD (P trend=0.01). Compared with>120 minutes, the adjusted hazard ratio (HR) associated with smoking timing was 1.28 (95% CI, 0.92-1.80) for 61-120 minutes, 1.48 (95% CI, 1.11-1.96) for 30-60 minutes, 1.36 (95% CI, 1.01-1.88) for 5-15 minutes, and 1.70 (95% CI, 1.22-2.37) for<5 minutes, respectively. Furthermore, there was a significant additive interaction and multiplicative interactions between the timing of smoking and a healthy diet score (P for additive interaction=0.01; P for multiplicative interaction = 0.004).
CONCLUSIONS: Generalizability, possible residual confounding, limiting causal inference.
CONCLUSIONS: These findings reveal a significant association between the shorter time from waking to the first cigarette and a higher CKD risk. The magnitude of these associations was greater in the setting of an unhealthy diet.
UNASSIGNED: This study explored the association of the daily timing of first cigarette smoking and the occurrence of kidney disease. Further, we addressed whether this association was influenced by the quality of the diet. The study found that smoking very soon after waking, especially when combined with a poorer quality diet, was associated with a significantly increased risk of developing chronic kidney disease. This research emphasizes the value of healthier lifestyle choices for kidney health.

BACKGROUND: Air pollutants are important exogenous stimulants to eye diseases, but knowledge of associations between long-term exposure to air pollutants and the risk of primary open-angle glaucoma (POAG) is limited. This study aimed to determine whether long-term exposure to air pollutants, genetic susceptibility, and their joint effects lead to an elevated risk of incident POAG.
METHODS: This is a population-based prospective cohort study from UK Biobank participants with complete measures of air pollution exposure and polygenetic risk scores. Cox proportional hazard models were fitted to assess the individual and joint effects of long-term exposure to air pollutants and genetics on the risk of POAG. In addition, the effect modification of genetic susceptibility was examined on an additive or multiplicative scale.
RESULTS: Among 434,290 participants with a mean (SD) age of 56.5 (8.1) years, 6651 (1.53 %) were diagnosed with POAG during a median follow-up of 13.7 years. Long-term exposure to air pollutants was associated with an increased risk of POAG. The hazard ratios associated with per interquartile range increase in PM2.5, PM2.5 absorbance, PM10, NO2, and NOX individually ranged from 1.027 (95 % CI: 1.001-1.054) to 1.067 (95 % CI: 1.035-1.099). Compared with individuals residing in low-pollution areas and having low polygenic risk scores, the risk of incident POAG increased by 105.5 % (95 % CI: 78.3 %-136.9 %), 79.7 % (95 % CI: 56.5 %-106.5 %), 103.2 % (95 % CI: 76.9 %-133.4 %), 89.4 % (95 % CI: 63.9 %-118.9 %), and 90.2 % (95 % CI: 64.8 %-119.5 %) among those simultaneously exposed to high air pollutants levels and high genetic risk, respectively. Genetic susceptibility interacted with PM2.5 absorbance and NO2 in an additive manner, while no evidence of multiplicative interaction was found in this study. Stratification analyses revealed stronger effects in Black people and the elderly.
CONCLUSIONS: Long-term air pollutant exposure was associated with an increased risk of POAG incidence, particularly in the population with high genetic predisposition.

UNASSIGNED: To examine a potential synergistic effect of history of childhood adversity and COVID-19 pandemic exposure on the association with mental health concerns in undergraduate students. Participants: We used U-Flourish Survey data from 2019 (pre-pandemic) and 2020 (during-pandemic) first-year cohorts (n = 3,149) identified at entry to a major Canadian University.
UNASSIGNED: Interactions between childhood adversity (physical and sexual abuse, and peer bullying) and COVID-19 pandemic exposure regarding mental health concern (depressive and anxiety symptoms, suicidality, and non-suicidal self-harm) were examined on an additive scale.
UNASSIGNED: We found a positive additive interaction between physical abuse and pandemic exposure in relation to suicidality (combined effect was greater than additive effect (risk difference 0.54 vs. 0.36)). Conversely, less than additive interactions between peer bullying and pandemic regarding depression and anxiety were observed.
UNASSIGNED: Childhood adversities have diverse reactions to adult stressor depending on the nature of the childhood adversity and the mental health outcomes.

UNASSIGNED: The aim of this study was to explore, on an additive scale, the combined effect of the association between insulin resistance (IR), chronic low-grade inflammation (CLGI) and vitamin D deficiency (VDD) on the risk of type 2 Diabetes Mellitus (T2DM).
UNASSIGNED: This is a cohort study, including 1484 non-diabetic subjects, followed for a period of four years. 25 hydroxy-vitamin D (25OHD), hypersensitive C-reactive protein (HsCRP) and triglyceride-glucose index were assessed. Based on VDD and CLGI, the population was subdivided into 4 exposure groups. Analysis was performed both in the case of IR and without IR. Cox proportional regression and additive interaction were applied to explore cumulative effects of exposure.
UNASSIGNED: At follow-up, 162 newly diagnosed cases of T2DM were identified. TYG index (RR = 4.0[2.8-5.6]), HsCRP (RR = 1.6 [1.4-1.7]) and 25OHD (RR = 0.96 [0.39-0.98]) were all significantly associated with the risk of T2DM (p < 0.01). The highest excess risk was recorded in patients cumulating simultaneously IR, CLGI and VDD (RR= 8.4[3.6-19.8], p < 0.0001). The additive interaction was significant, the excess risk linked to the interaction RERI = 10.5[1.43-19.7], the proportion attributable to the combined effect: AP = 0.61[0.37-0.85], and the interaction was synergistic: synergy index: 2.8[1.42-5.69].
UNASSIGNED: Baseline levels of TYG index, 25OHD and HsCRP are strongly predictive of future T2DM, and their joint effects are additive and synergistic. Interventional studies are therefore warranted in order to evaluate whether vitamin D supplementation, combined with appropriate anti-inflammatory therapies, is effective as a preventive strategy to reduce the risk of T2DM.