Publications by authors named "Leo Lahti"

Background: In India, joint families often encompass members spanning multiple generations cohabiting in the same household, thereby sharing the same ethnicity, genetics, dietary habits, lifestyles, and other living conditions. Such an extended family provides a unique opportunity to evaluate the effect of genetics and other confounding factors like geographical location, diet and age on the skin microbiota within and between families across three generations.

Methods: The present study involved seventy-two individuals from fifteen families from two geographical regions of Maharashtra, India.

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The effect of fermented foods on healthy human gut microbiota structure and function, particularly its seasonal preference and frequent long-term consumption, has been largely uncharacterised. Here, we assess the gut microbiota and metabolite composition of 78 healthy Indian agrarian individuals who differ in the intake of fermented milk and soybean products by seasonal sampling during hot-humid summer, autumn and dry winter. Here we show that, seasonal shifts between the Prevotella- and Bifidobacterium/Ruminococcus-driven community types, or ecological states, and associated fatty acid derivatives, with a bimodal change in Bacteroidota community structure during summer, particularly in fermented milk consumers.

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Background: Maternal stress can have short and long term adverse (mental) health effects for the mother and her child. Previous evidence suggests that the gut microbiota may be a potential mediator and moderator for the effects of stress via various pathways. This study explored the maternal microbiota trajectory during pregnancy as well as the association between pre- and postnatal maternal stress and features of the maternal and infant gut microbiota during and after pregnancy.

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The COVID-19 pandemic has presented unprecedented challenges worldwide, necessitating effective modelling approaches to understand and control its transmission dynamics. In this study, we propose a novel approach that integrates asymptomatic and super-spreader individuals in a single compartmental model. We highlight the advantages of utilizing incommensurate fractional order derivatives in ordinary differential equations, including increased flexibility in capturing disease dynamics and refined memory effects in the transmission process.

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Background/objectives: Obesity impairs intestinal glucose uptake (GU) (intestinal uptake of circulating glucose from blood) and alters gut microbiome. Exercise improves intestinal insulin-stimulated GU and alters microbiome. Genetics influence the risk of obesity and gut microbiome.

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Article Synopsis
  • Human microbiomes play a crucial role in health by impacting metabolism, immune functions, and neurological processes, but their complete complexity is still not fully understood.
  • The definition of a "healthy" microbiome is controversial due to variations in microbial communities and the difficulty in establishing a standard definition for health across different individuals and conditions.
  • The article highlights progress in microbiome research and identifies gaps in knowledge, proposing a roadmap that utilizes epidemiological methods to better understand the relationship between microbiomes and health.
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Obesity and sedentarism are associated with increased liver and pancreatic fat content (LFC and PFC, respectively) as well as impaired organ metabolism. Exercise training is known to decrease organ ectopic fat but its effects on organ metabolism are unclear. Genetic background affects susceptibility to obesity and the response to training.

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The gut microbiota (GM) can regulate bone mass, but its association with incident fractures is unknown. We used Cox regression models to determine whether the GM composition is associated with incident fractures in the large FINRISK 2002 cohort (n = 7043, 1092 incident fracture cases, median follow-up time 18 years) with information on GM composition and functionality from shotgun metagenome sequencing. Higher alpha diversity was associated with decreased fracture risk (hazard ratio [HR] 0.

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Article Synopsis
  • Antibiotic resistance genes (ARGs) are commonly found in the gut microbiota of infants, contributing to a reservoir of ARGs that evolve as the microbiome develops in early life.
  • A study analyzing 2,328 stool samples from 475 children revealed that infants share more similar gut ARG compositions with their mothers and spouses than with their fathers, indicating family influences on ARG dynamics.
  • The research shows that early gut microbiota development is a significant factor in ARG load, with caesarean-delivered infants having lower ARG levels, highlighting that the typical evolution of gut bacteria has a stronger impact on ARGs than recent antibiotic use.
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Although knowledge of the role of the oral microbiome in ischemic stroke is steadily increasing, little is known about the multikingdom microbiota interactions and their consequences. We enrolled participants from a prospective multicentre case-control study and investigated multikingdom microbiome differences using saliva metagenomic datasets ( = 308) from young patients diagnosed with cryptogenic ischemic stroke (CIS) and age- and sex-matched stroke-free controls. Differentially abundant taxa were identified using Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC2).

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Article Synopsis
  • - The study investigated how changes in gut microbiota might influence the risk of future hospitalization due to infections in two large groups from the Netherlands and Finland, focusing on individuals aged 18-74.
  • - Researchers used 16S rRNA gene sequencing to analyze gut microbiota from participants' fecal samples and looked for links between microbiota characteristics (like diversity and butyrate-producing bacteria) and infection-related health outcomes over a follow-up period of 5-7 years.
  • - The results included data from 10,699 participants, revealing potential relationships between certain microbiota profiles and increased susceptibility to severe infections, although further clarification on these interactions in humans is needed.
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Executive Summary: Microbes are all pervasive in their distribution and influence on the functioning and well-being of humans, life in general and the planet. Microbially-based technologies contribute hugely to the supply of important goods and services we depend upon, such as the provision of food, medicines and clean water. They also offer mechanisms and strategies to mitigate and solve a wide range of problems and crises facing humanity at all levels, including those encapsulated in the sustainable development goals (SDGs) formulated by the United Nations.

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The early gut microbiota composition is fundamentally important for piglet health, affecting long-term microbiome development and immunity. In this study, the gut microbiota of postparturient dams was compared with that of their offspring in three Finnish pig farms at three growth phases. The differences in fecal microbiota of three study development groups (Good, Poorly, and PrematureDeath) were analyzed at birth (initial exposure phase), weaning (transitional phase), and before slaughter (stable phase).

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Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups.

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Article Synopsis
  • This study explores the effectiveness of combining genomic and gut metagenomic assessments to better predict the risk of diseases like coronary artery disease, type 2 diabetes, Alzheimer’s, and prostate cancer, using data from a long-term health record follow-up.
  • The researchers found that using polygenic risk scores (PRSs) significantly enhances disease prediction compared to traditional risk factors alone.
  • By integrating PRSs with gut microbiome scores and conventional risk factors, they achieved the best predictive outcomes for the diseases studied, highlighting the potential of multiomics in early disease detection and risk profiling.
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Introduction: Gut microbiome-derived nanoparticles, known as bacterial extracellular vesicles (bEVs), have garnered interest as promising tools for studying the link between the gut microbiome and human health. The diverse composition of bEVs, including their proteins, mRNAs, metabolites, and lipids, makes them useful for investigating diseases such as cancer. However, conventional approaches for studying gut microbiome composition alone may not be accurate in deciphering host-gut microbiome communication.

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Background: Dietary fiber is an important health-promoting component of the diet, which is fermented by the gut microbes that produce metabolites beneficial for the host's health.

Objectives: We studied the associations of habitual long-term fiber intake from infancy with gut microbiota composition in young adulthood by leveraging data from the Special Turku Coronary Risk Factor Intervention Project, an infancy-onset 20-y dietary counseling study.

Methods: Fiber intake was assessed annually using food diaries from infancy ≤ age 20 y.

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The gut microbiota is vital for human body development and function. Its development in early life is influenced by various environmental factors. In this randomized controlled trial, the gut microbiota was obtained as a secondary outcome measure in a study on the effects of one hour of daily skin-to-skin contact (SSC) for five weeks in healthy full-term infants.

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Manipulative behaviour that consists of touching or close contact with ears or tails of pen mates is common in pigs and can become damaging. Manipulative behaviour was analysed from video recordings of 45-day-old pigs, and 15 manipulator-control pairs (n = 30) were formed. Controls neither received nor performed manipulative behaviour.

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Article Synopsis
  • The human microbiome is a key area of research for understanding health, but analyzing its complex data is challenging.
  • Machine learning (ML) algorithms are being developed to help process this data, uncover patterns, and create predictive models.
  • This review catalogs ML-based software tools for microbiome analysis, providing insights, usage examples, and highlighting areas that need improvement for both developers and users.
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The notions of , such as birth, death, growth, evolution and longevity, extend across reality, including biological, cultural and societal phenomena. Patterns of change describe how success and composition of every entity, from species to societies, vary across time. Languages develop into new languages, music and fashion continuously evolve, economies rise and decline, ecological and societal crises come and go.

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