| 1. |
- Marabita, Francesco, et al.
(författare)
-
Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health
- 2022
-
Ingår i: Cell Systems. - : Cell Press. - 2405-4712 .- 2405-4720. ; 13:3, s. 241-255.e7
-
Tidskriftsartikel (refereegranskat)abstract
- We explored opportunities for personalized and predictive health care by collecting serial clinical measurements, health surveys, genomics, proteomics, autoantibodies, metabolomics, and gut microbiome data from 96 individuals who participated in a data-driven health coaching program over a 16-month period with continuous digital monitoring of activity and sleep. We generated a resource of >20,000 biological samples from this study and a compendium of >53 million primary data points for 558,032 distinct features. Multiomics factor analysis revealed distinct and independent molecular factors linked to obesity, diabetes, liver function, cardiovascular disease, inflammation, immunity, exercise, diet, and hormonal effects. For example, ethinyl estradiol, a common oral contraceptive, produced characteristic molecular and physiological effects, including increased levels of inflammation and impact on thyroid, cortisol levels, and pulse, that were distinct from other sources of variability observed in our study. In total, this work illustrates the value of combining deep molecular and digital monitoring of human health. A record of this paper's transparent peer review process is included in the supplemental information.
|
|
| 2. |
- Haghighi, Mona, et al.
(författare)
-
A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study
- 2016
-
Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 6
-
Tidskriftsartikel (refereegranskat)abstract
- Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.
|
|
| 3. |
- Männistö, Elisa, et al.
(författare)
-
Emissions of biogenic volatile organic compounds from adjacent boreal fen and bog as impacted by vegetation composition
- 2023
-
Ingår i: Science of the Total Environment. - : Elsevier BV. - 0048-9697. ; 858
-
Tidskriftsartikel (refereegranskat)abstract
- Peatland ecosystems emit biogenic volatile organic compounds (BVOC), which have a net cooling impact on the climate. However, the quality and quantity of BVOC emissions, and how they are regulated by vegetation and peatland type remain poorly understood. Here we measured BVOC emissions with dynamic enclosures from two major boreal peatland types, a minerotrophic fen and an ombrotrophic bog situated in Siikaneva, southern Finland and experimentally assessed the role of vegetation by removing vascular vegetation with or without the moss layer. Our measurements from four campaigns during growing seasons in 2017 and 2018 identified emissions of 59 compounds from nine different chemical groups. Isoprene accounted for 81 % of BVOC emissions. Measurements also revealed uptake of dichloromethane. Total BVOC emissions and the emissions of isoprene, monoterpenoids, sesquiterpenes, homoterpenes, and green leaf volatiles were tightly connected to vascular plants. Isoprene and sesquiterpene emissions were associated with sedges, whereas monoterpenoids and homoterpenes were associated with shrubs. Additionally, isoprene and alkane emissions were higher in the fen than in the bog and they significantly contributed to the higher BVOC emissions from intact vegetation in the fen. During an extreme drought event in 2018, emissions of organic halides were absent. Our results indicate that climate change with an increase in shrub cover and increased frequency of extreme weather events may have a negative impact on total BVOC emissions that otherwise are predicted to increase in warmer temperatures. However, these changes also accompanied a change in BVOC emission quality. As different compounds differ in their capacity to form secondary organic aerosols, the ultimate climate impact of peatland BVOC emissions may be altered.
|
|
| 4. |
- Qian, Frank, et al.
(författare)
-
n-3 Fatty Acid Biomarkers and Incident Type 2 Diabetes : An Individual Participant-Level Pooling Project of 20 Prospective Cohort Studies
- 2021
-
Ingår i: Diabetes Care. - : American Diabetes Association. - 0149-5992 .- 1935-5548. ; 44:5, s. 1133-1142
-
Tidskriftsartikel (refereegranskat)abstract
- OBJECTIVE Prospective associations between n-3 fatty acid biomarkers and type 2 diabetes (T2D) risk are not consistent in individual studies. We aimed to summarize the prospective associations of biomarkers of alpha-linolenic acid (ALA), eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA) with T2D risk through an individual participant-level pooled analysis.RESEARCH DESIGN AND METHODS For our analysis we incorporated data from a global consortium of 20 prospective studies from 14 countries. We included 65,147 participants who had blood measurements of ALA, EPA, DPA, or DHA and were free of diabetes at baseline. De novo harmonized analyses were performed in each cohort following a pre-specified protocol, and cohort-specific associations were pooled using inverse variance-weighted meta-analysis.RESULTS A total of 16,693 incident T2D cases were identified during follow-up (median follow-up ranging from 2.5 to 21.2 years). In pooled multivariable analysis, per interquintile range (difference between the 90th and 10th percentiles for each fatty acid), EPA, DPA, DHA, and their sum were associated with lower T2D incidence, with hazard ratios (HRs) and 95% CIs of 0.92 (0.87, 0.96), 0.79 (0.73, 0.85), 0.82 (0.76, 0.89), and 0.81 (0.75, 0.88), respectively (all P < 0.001). ALA was not associated with T2D (HR 0.97 [95% CI 0.92, 1.02]) per interquintile range. Associations were robust across prespecified subgroups as well as in sensitivity analyses.CONCLUSIONS Higher circulating biomarkers of seafood-derived n-3 fatty acids, including EPA, DPA, DHA, and their sum, were associated with lower risk of T2D in a global consortium of prospective studies. The biomarker of plant-derived ALA was not significantly associated with T2D risk.
|
|
