302 research outputs found
Combined Nutrition and Exercise Interventions in Community Groups
Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews
Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea
ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK
The Application of Simulation to Quantifying the Influence of Bias in Perinatal Epidemiology
Perinatal aetiological associations derived from observational data are susceptible to various types of bias. This thesis demonstrated the application of simulation methodologies to quantify the influence of bias in perinatal epidemiology through a series of simulation studies which quantified the magnitude and direction of bias mechanisms. A framework to guide epidemiologists in the development, implementation and reporting of simulation studies to quantify bias was developed. Simulation is a potent tool to the quantification of bias
Maternal metabolic health and neurodevelopmental conditions in offspring
Background
Observational studies published in the last decade have indicated relationships between
maternal “overnutrition” states and offspring neurodevelopmental conditions (NDCs), such as
autism, attention deficit/hyperactivity disorder (ADHD), and intellectual disability (ID).
“Maternal overnutrition” states have been characterized by a series of metabolic conditions
before pregnancy (i.e., overweight/obesity, Type I [T1DM] and II [T2DM] diabetes) and during
pregnancy (i.e., gestational diabetes mellitus [GDM] and excessive gestational weight gain
[GWG]). NDCs often co-occur and have multifactorial etiologies, shaped by both genetic and
environmental factors. However, previous studies have not thoroughly considered these
complex etiologies when examining associations. For instance, they did not explore whether
the relationships between maternal diabetes and offspring NDCs could differ based on the cooccurrence
of NDCs or be influenced by genetic predispositions. Moreover, as the fetal brain
evolves dramatically and sequentially during pregnancy, it accentuates the need for
epidemiological studies to account for the timing and intensity of perturbations during this
period. Prior research hasn’t determined whether relationships between maternal conditions
such as excessive GWG or hyperglycemia and offspring NDCs could differ based on the GWG
rate and glucose concentrations at different pregnancy phases. Maternal overweight/obesity and
diabetes might be associated with offspring NDCs due to complications encountered during
pregnancy, childbirth, and the neonatal period. Research has suggested that these complications
lie at the intersection of, and relate to, maternal metabolic conditions and offspring NDCs.
Grasping the mediation of these complications can offer deeper insights into preventative
measures during these stages; however, no studies have yet quantified these complications’
mediating impact on the associations. Lastly, the causal influence of BMI, including maternal
BMI, on offspring autism and ADHD has seldom been thoroughly explored. In the absence of
compelling evidence, the question remains as to whether better weight management among
obese women before conception could help reduce the potential risks of offspring NDCs.
Methods
We used two databases, “Psychiatry Sweden (PS), 1987-2016” and “Developmental Origins of
Health And Disease (DOHAD), 1997-2021”, which are register linkages across Swedish
nationwide registers using the unique identification number assigned to each Swedish resident.
Offspring were linked to their biological mothers, fathers, and maternal grandparents using the
Total Population Register (Study I, IV, V). We also used a series of maternal weight and
capillary glucose records across pregnancy from the Stockholm Obstetrix system, an electronic
medical journal of antenatal care, which was nested within the “Stockholm Youth Cohort
(SYC)”. The SYC is a part of PS that also includes regional health and administrative registers
(Study II, III). In Studies I, IV, and V, we used the National Patient Register to identify
offspring NDCs (i.e., autism, ADHD, and ID), which was supplemented by regional register
information in Studies II and III as well as the National Prescribed Drug Register (for ADHD).
Finally, we utilized genetic data and information from mothers and children in the “Avon
Longitudinal Study of Parents and Children (ALSPAC)” cohort (Study V).
In Study I, we utilized a generalized estimating equation (GEE)/population average model with
a logit link. This model was clustered based on pseudonymized maternal identification numbers
and employed robust standard errors for the computation of odds ratios (ORs) and 95%
confidence intervals (CIs) regarding neurodevelopmental conditions (NDCs) in offspring. In
Study II, we used Cox regression models, again clustered on maternal numbers and with robust
standard errors, to determine hazard ratios (HRs) and 95% CIs for offspring NDCs. In Study
III, we employed group-based trajectory modeling (GBTM) to ascertain the varying patterns
of glucose alteration throughout pregnancy. GEE models were utilized to evaluate the
associations with both obstetric and neonatal outcomes and offspring NDCs. In Study IV, we
used a parametric regression approach within a counterfactual framework to conduct both single
and multiple mediation analyses. This study aimed to quantify the total effect (TE), natural
indirect effects (NIE), and natural direct effects (NDE) in the associations of maternal diabetes
(both pregestational diabetes mellitus [PGDM] and GDM) and overweight/obesity with NDCs
through individual components of mediators. We employed a paternal negative control
comparison analysis in Study I to examine if the associations of maternal T1DM and T2DM
with offspring NDCs could be confounded by genetic predispositions to diabetes and NDCs. In
Study V, we applied a “triangulation” approach. Analyses were performed using maternal
cousin and full sibling comparisons to address unobserved, shared genetic and environmental
factors in the associations between maternal BMI and offspring autism and ADHD. In addition,
we explored the genetic correlation through Linkage Disequilibrium Score Regression (LDSC).
Moreover, we examined the association between the genetic predisposition to both maternal
and children’s BMI and various traits of children’s autism and ADHD using Polygenic Risk
Score (PRS) analysis. Lastly, we employed a two-sample Mendelian randomization analysis
(MR) in Study V to evaluate the causal impacts of BMI on NDCs, including autism and ADHD.
Results
Maternal T1DM, T2DM, and GDM were all associated with offspring autism, ADHD, and ID,
with greater risks linked to comorbid diagnoses involving ID. Stronger associations with GDM
were observed when diagnosed between 27-30 wkGA. Paternal T1DM and T2DM were also
associated with offspring NDCs, though the strength of these associations was less than those
observed with maternal diabetes (Study I). Lower rates of GWG in the second trimester and
higher rates of GWG in the third trimester were associated with increased risks for offspring
NDCs (Study II). Among those without PGDM, persistently high glucose levels throughout
pregnancy demonstrated the strongest association with adverse obstetric/neonatal
complications. Transient hyperglycemic states followed by periods of potential glycemic
control were also associated with these complications but to a lesser extent. Notably, subclinical
states of hyperglycemia, which were less likely to receive a GDM diagnosis, remained
associated with these complications, albeit to a lesser degree. A similar pattern of associations
was observed for offspring NDCs. Persistently high glucose levels showed stronger
associations with offspring NDCs (i.e., ADHD only), while weaker associations were identified
with transient hyperglycemic states followed by improved glucose control. Notably, we found
that hyperglycemia in early pregnancy, but not in mid-pregnancy, was associated with offspring
NDCs when followed by improved glucose control. However, none of these associations
regarding NDC outcomes survived the false discovery rate correction using the Benjamini-
Hochberg approach (Study III). The joint mediating effects of all obstetric and neonatal
complications were more pronounced in the associations between PGDM and offspring NDCs
(accounting for 30-50% of the association) than in those concerning maternal GDM and
overweight/obesity. Although the mediating effects of obstetric and neonatal complications
were generally insignificant for GDM and minor for maternal overweight/obesity, we observed
direct associations between GDM (10-30% increased risks compared to non-diabetes) and
maternal overweight/obesity (30-60% increased risks compared to normal weight) with the
risks of offspring NDCs. However, these associations might still contain residual confounding
due to unobserved factors. The combined mediating effects of these complications, especially
those emerging during the neonatal period, were particularly strong in the relationship between
maternal PGDM and offspring NDCs. For individual mediators, the effects were generally
minimal, except for complications such as pregnancy hypertensive diseases, preterm birth,
neonatal asphyxia, and hematological comorbidities in the association between PGDM and
offspring NDCs (with proportions mediated exceeding 10%) (Study IV). Maternal obesity was
linked to increased risks of autism and ADHD in both the full cohort analysis and family designs
(i.e., maternal cousin comparisons and full sibling analyses). It is worth noting that when
accounting for shared familial factors in family designs, the associations were attenuated but
modest associations remained. For instance, among full siblings, children exposed to maternal
obesity had a 0.87% higher risk of autism and a 2.13% higher risk of ADHD at age 16,
compared to those exposed to mothers of normal weight. The LDSC analysis showed a genetic
correlation between BMI and ADHD, but not with autism. The PRS analysis provided less
evidence suggesting a relationship between maternal and children’s genetic liability to BMI and
various autism and ADHD traits. Specifically, a one-unit increase in BMI was associated with
a 12% higher risk for autism and a 77% increased risk for ADHD (Study V).
Conclusions
In conclusion, my research has reaffirmed known associations between maternal metabolic
conditions and offspring NDCs while providing new insights into the underlying mechanisms
and causal relationships. Greater associations between maternal diabetes and NDCs involving
ID suggested distinct pathophysiological mechanisms. The associations involving PGDM and
offspring NDCs might be partially confounded by a genetic predisposition to both the exposure
and outcomes; however, its intrauterine effects cannot be completely discounted. Further
investigation into the causal link is still warranted in the future. To reduce the risk of offspring
NDCs associated with maternal PGDM, it can be beneficial to manage specific obstetric and
neonatal complications, especially those arising during the neonatal period. For maternal GDM
and overweight/obesity, although we found direct associations with NDCs without evident
mediating effects from obstetric and neonatal complications, these direct associations might
still contain residual confounding due to unobserved factors. GDM during weeks 27-30 of
gestation showed a more pronounced association with offspring NDCs. However, women with
hyperglycemia in mid-pregnancy who subsequently achieved effective glucose control did not
have a notable increased risk of NDCs among their offspring. While this suggests that effective
glucose management during mid-pregnancy might benefit offspring neurodevelopment, further
studies are needed to confirm a causal link. My research provides evidence of a modest causal
relationship between maternal BMI and offspring autism and ADHD. Further, a lower rate of
GWG in the second trimester and a higher rate in the third trimester were more strongly
associated with offspring NDCs. This suggests that continuous monitoring and potential
interventions related to weight and weight gain from conception onward could have a positive,
albeit modest, impact on reducing the risks of offspring NDCs, such as autism and ADHD
The Impact of Dietary Macronutrient Composition on Noncommunicable Diseases and Aging: A Life Course Approach
Background: Nutrition is vital for human health and is a key modifiable risk factor in the development of noncommunicable diseases (NCDs), which account for 74% of global annual deaths. Aging increases disease risk, and nutritional associations vary across life stages. However, current research often focuses on individual nutrients rather than complex associations. Therefore, this thesis investigates the association of macronutrients with NCDs and aging throughout the lifespan using a multi-nutrient approach known as the Geometric Framework for Nutrition.
Aims: Six studies were conducted to explore the following aims at varying stages of the life course:
1) How are macronutrients linked to NCDs?
2) Is dietary macronutrient composition associated with markers of biological aging?
3) What roles do diet quality, food groups, and factors like the microbiome play in macronutrient-NCD and aging relationships?
Results: Findings from this thesis revealed a complex nonlinear relationship for macronutrients with aging (Chapters 4, 6), metabolic health (Chapters 4, 5), and disease outcomes (Chapter 7). These relationships suggest that there is no single optimal macronutrient composition for all outcomes. Notably, the microbiome was shown to play a potential effect-modifying role in how diet impacts cardiometabolic health (Chapter 8). Furthermore, the final study revealed that macronutrient composition associations with NCDs widely differ according to diet quality (Chapter 9).
Conclusions: Dietary macronutrient composition is intricately linked to metabolic health, aging, and NCD risk, with variations based on diet quality, life stage, and potential modification by factors like the microbiome. The findings emphasize the need for a comprehensive and standardized approach to nutritional research that considers each of these aspects before providing dietary guidance or making public health recommendations
Mind the gap:Socioeconomic health inequalities in early life
Health differences between most and least advantaged become apparent right from the earliest stages of life, manifesting as adverse birth outcomes. The overarching aim of this thesis was to delve into the complex relationship between socioeconomic status (SES) and early-life health, with a particular focus on the role played by neighbourhood-level socioeconomic conditions
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