Can Molecular Pathology Drive Progress in Microbiome Understanding? Lessons from Spousal and Household Studies

Abstract

The human microbiome is often presented as “the next genetics,” with the expectation that microbial profiles will explain complex diseases and yield new therapies. Yet for most conditions, it remains unclear whether microbiome changes act as causal drivers or primarily mirror underlying host biology and pathology. In this narrative review, we argue that microbiome causality is frequently overstated relative to the roles of host genetics and the environment, and we explore the implications for molecular pathology. We outline a simple framework in which the microbiome can act as (i) a primary driver, (ii) a conditional mediator or effect modifier or (iii) an association biomarker that mainly reflects upstream processes. We then use marital and household studies as natural experiments to test whether chronic diseases track more strongly with a shared microbiome or with a shared lifestyle and host susceptibility. Across metabolic, inflammatory, neurodegenerative and ageing-related outcomes, spouses show only low to modest disease concordance, which is difficult to reconcile with a universally strong, transmissible microbiome causality. Adult microbiomes instead appear mostly host-constrained and context-dependent, acting more as destabilisers of homeostasis and amplifiers of allostatic load than as independent disease-causing factors. For molecular pathology, this suggests that microbiome features are often most informative as biomarkers integrated alongside host genomics, immune context and histopathology, rather than as standalone targets. Study designs and diagnostic workflows should therefore jointly model the host genome, environment, behaviour and microbiome within broader systems medicine frameworks.

1. Introduction

Over the past decade, the human microbiome has moved from a niche topic to a central narrative in biomedicine, fuelled by large-scale efforts such as the Human Microbiome Project and its successor, the Integrative Human Microbiome Project (iHMP). These projects demonstrated striking inter-individual variability and strong site specialisation of microbial communities across gut, skin, oral and vaginal niches, even among healthy people [1,2,3]. The success and visibility of these initiatives, combined with compelling mechanistic data from animal models, have encouraged a narrative that the microbiome is a flexible, tractable “organ” with broad explanatory and therapeutic potential. At the same time, critical reflections have highlighted how microbiome research has raised expectations that far outpace robust causal evidence, with comparatively few clinically transformative interventions emerging outside of recurrent Clostridioides difficile infection [4,5,6]. Media analyses also show that popular coverage often reinforces an optimistic “gut health” narrative, rarely communicating uncertainty or the difficulty of inferring causality [7].

The question of microbiome causality in disease is not a new one. Previous studies have repeatedly raised concerns about the lack of strict methodological frameworks and the potential for spurious associations in microbiome-wide studies [8,9]. Mendelian randomisation (MR) and related approaches now provide opportunities to separate causal from non-causal roles by asking (i) to what extent host genetic variants shape microbiome composition and (ii) whether microbiome-associated variants predict disease risk in ways compatible with a causal role for the microbiome [10,11]. These approaches converge on a simple but often neglected question: in a broad sense, is the human microbiome “transmissible”? Here, we use the term “transmissible” to mean that microbiome configurations are passed between individuals and can independently drive disease risk, beyond simply reflecting shared environments or host predispositions. If the answer is yes, then the microbiome should be measured, analysed and targeted in interventional studies as an independent modifiable risk factor. If not, then inter-individual microbiome differences must be driven predominantly by underlying host factors (for example, genetics and immunity) and environmental exposures, which define both disease risks and microbiome profiles.

An overview of these hypotheses confirms some microbiome sharing between spouses. In a community-based study of older adults, spouse pairs had more similar gut microbiomes, shared more bacterial taxa than siblings or unrelated individuals and greater social connectedness correlated with microbiome composition [12]. Skin microbiome profiling of cohabiting couples likewise shows that household membership is a strong determinant of community structure, with partners’ skin communities more similar to each other’s than to those of non-cohabitants [13]. However, more detailed analyses have shown a much lower degree of sharing than might be expected: between about 10% and 30% of bacterial strains are transmitted even in cases of close and intimate contact, with stronger sharing in closer spousal relationships [12,13,14]. Together, these studies reinforce the idea that the environment and intimate contact drive spousal microbiome convergence, and that marital units share sizable but far from identical fractions of their microbiomes. This suggests that only a subset of the microbiome can be reasonably considered transmittable, while the majority remains individual and host-constrained.

In this review, we propose a simple framework for classifying microbiome–disease relationships into primary drivers, conditional mediators or effect modifiers and association biomarkers. We then use marital and household designs, where partners share an environment and part of their microbiome but not their genome, as a natural experiment to evaluate whether patterns of disease concordance are more compatible with a strongly transmittable or a predominantly host-constrained microbiome. Finally, we discuss mechanistic implications in terms of homeostasis and allostatic load, and we outline priorities for study design and clinical translation. These questions are particularly salient for molecular pathology. Tissue-based detection of microbial signatures, such as intratumoural bacteria, viral oncogenesis or microbiome-derived metabolites are more frequently integrated into diagnostic and predictive workflows. However, without a clear framework for when microbiome differences are causal, there is a risk of over-interpreting tissue-level microbial findings and misdirecting biomarker development.

2. Conceptualising Microbiome Causality

A central conceptual problem in establishing a causal role of the microbiome in complex diseases is that microbiome–disease relationships are often treated as a binary attribute: either the microbiome is causal or it is not. This reductionist view obscures the spectrum of possible causal roles and inhibits a broader understanding of how microbial communities participate in disease processes. Instead, it is more useful to consider multiple levels of causality.

2.1. Microbiome as Primary Disease Risk Driver

In the primary driver role, the microbiome is a key determinant of disease: altering the microbiome alone is sufficient to produce large effects on disease risk or course, and the disease rarely arises without that microbial state (Figure 1). The most obvious human example is recurrent C. difficile infection, where faecal microbiome transplantation can dramatically alter relapse risk with large effect sizes compared with standard antibiotic therapy [15].

2.2. Conditional Mediator or Effect Modifier

In many other conditions, the microbiome appears to influence disease only conditional upon other factors, including particular host genotypes, immune states or environmental exposures (Figure 1). In this role, the microbiome may lie on the pathway between an upstream cause and an outcome (mediator), or it may alter the strength or direction of an exposure’s effect (effect modifier). For example, host genetic variants that affect bile acid metabolism or mucosal immunity can change both microbiome composition and infection risk, making microbiome composition partly a reflection of underlying host biology [16,17].

2.3. Association Biomarker

Finally, in many settings, the microbiome predominantly mirrors host processes such as immune activation, metabolic state, medication use, diet or frailty, without being required for the disease to appear or for progression to occur (Figure 1). Numerous microbiome signatures in metabolic syndrome, cardiovascular disease and neurodegeneration are currently best placed in this category: they are reproducibly associated, but causal direction and effect size remain uncertain [6,18]. In such cases, high-performing microbiome-based classifiers can arise through reverse causality and confounding and do not by themselves imply that manipulating the microbiome will meaningfully modify disease risk.

Notably, these three roles may not be mutually exclusive and may differ or even overlap across disease stages or subgroups. One possible example of overlap between these mechanisms is systemic inflammation that impairs barrier integrity, which can in turn facilitate harmful bacterial metabolite uptake and consequently increase disease risk [19].

2.4. Methods and Search Strategy

In order to explore this topic further, we performed a narrative review that synthesises evidence from human microbiome, genetic epidemiology and epidemiological concordance studies to address whether the microbiome predominantly acts as a causal factor or as a disease marker. We focused on four main bodies of literature: (i) large-scale human microbiome surveys and multi-omics cohorts, (ii) microbiome genome-wide association studies (mGWAS) and twin/family designs, (iii) Mendelian randomisation and other genetic epidemiology studies linking microbiome features to complex diseases and (iv) epidemiological studies of spousal, household and familial concordance for chronic diseases.

Relevant publications were identified primarily through PubMed searches using combinations of terms such as microbiome, microbiota, spousal, conjugal, household, twin, familial, heritability, Mendelian randomisation, longevity, neurodegenerative, inflammatory bowel disease, metabolic syndrome and cardiometabolic. Additional studies were located by screening reference lists of key articles and prior reviews and by forward citation tracking of landmark papers. We prioritised human studies with clearly defined designs and, where available, those with larger sample sizes or robust longitudinal follow-up.

Because the aim of this work is conceptual rather than to exhaustively catalogue all microbiome–disease associations, we did not perform a formal systematic review or meta-analysis, and no quantitative synthesis was attempted. Instead, inclusion was based on relevance to the central questions of microbiome causality versus biomarker status, the availability of comparable spousal and familial risk estimates and the potential to illustrate the three proposed causal roles: primary driver, conditional mediator or effect modifier and association biomarker.

3. Mechanistic Framework: Homeostasis, Allostasis and Allostatic Load

From the perspective of disease mechanisms, the microbiome can perturb both homeostasis and allostasis. Homeostasis refers to the dynamic regulation of internal variables within relatively narrow ranges despite external perturbations, a concept classically articulated by Cannon [20]. By contrast, allostasis describes how organisms achieve “stability through change” by altering set-points and response profiles across interconnected systems in anticipation of, or adaptation to, environmental demands [21,22]. Prolonged or dysregulated allostatic responses generate allostatic load, i.e., the cumulative “wear and tear” on neural, endocrine, immune and metabolic systems that increases long-term disease risk [23]. In molecular pathology, this load is reflected in chronic low-grade inflammation, altered stress-hormone signalling, persistent immune activation or tissue remodelling [24].

Within this framework, the microbiome can be viewed as both a sensor and an amplifier of cumulative stressors such as diet, antibiotics, chronic inflammation, sleep disruption and psychosocial stress. Microbial communities respond to these exposures through shifts in composition and function, altering the production of metabolites (for example, short-chain fatty acids, bile acid derivatives, neurotransmitter precursors), microbe-associated molecular patterns and signalling through pattern-recognition receptors. These signals feed back into host systems by modulating epithelial barrier integrity, mucosal and systemic immune tone, neuroendocrine axes and metabolic pathways. At the tissue level, this can manifest as increased epithelial permeability, inflammatory infiltrates, microglial activation, endothelial dysfunction or fibrosis, all of which are familiar readouts in routine histopathology and molecular profiling.

Experimental and clinical work has begun to map these loops in specific disease contexts. Diet, stress and other lifestyle factors reshape the microbiome and feed into neuroinflammation and neurodegeneration [25], microbiome-mediated barrier disruption and immune activation have been implicated in stress-related psychiatric disorders [26] and stress-induced changes in gut microbiome, permeability and immune signalling can tip gastrointestinal physiology toward disease [27,28]. Recent reviews further highlight how gut microbes sense and integrate chronic stress and circadian/sleep disruption, feeding back via metabolites and hypothalamic–pituitary–adrenal and circadian circuitry [29]. In this framing, the microbiome rarely acts alone; instead, it tends to contribute to disease when host defences are already compromised and allostatic load is high. This perspective aligns naturally with a view of the microbiome as a conditional mediator or amplifier of host stress responses rather than an autonomous disease-causing organ.

4. The Human Microbiome as a Host-Constrained, Environmentally Modulated System

The Human Microbiome Project demonstrated that healthy individuals differ markedly in microbial taxa across body sites. Despite this, within each major habitat (gut, skin, oral and vaginal), communities are strongly niche-structured and show lower within-person temporal variability than between-person variability in both taxonomy and function [1]. Longitudinal deep-sampling work has confirmed that, in adults, many strains are long-term “residents.” Time-series analyses showed that interpersonal differences dominate over day-to-day fluctuations and that most gut strains persist for years or decades in the absence of major perturbation [30]. These observations are consistent with a host-constrained ecosystem in which immune, nutritional and ecological filters shape stochastic colonisation.

The Integrative HMP extended this view by coupling multi-omics host data (genomics, transcriptomics, metabolomics, immune phenotyping) with longitudinal microbiome profiles in pregnancy, inflammatory bowel disease and prediabetes. These projects demonstrated that microbial shifts occur in synchrony with changes in host pathways, such as inflammatory signalling, barrier function and metabolic control and that the same clinical phenotype can be achieved via multiple host–microbe configurations [3]. In other words, microbiome dynamics are embedded in broader physiological networks rather than operating as an autonomous layer.

Microbiome genome-wide association studies (mGWAS), twin designs and large cohorts now converge on a “modest but non-trivial” role for host genetics. Twin and family studies estimate heritabilities for individual taxa and pathways typically in the 0.05–0.4 range, with a minority of taxa (e.g., Christensenellaceae, certain Bifidobacterium and Methanobrevibacter lineages) showing robust heritability, but most of the community appearing non-heritable [16,31].

Large mGWAS consortia (e.g., MiBioGen and Dutch Microbiome Project) have identified dozens to hundreds of host loci associated with specific microbes and functions, including lactase (LCT) variants influencing Bifidobacterium and bile acid–related loci shaping microbial metabolism, but these loci collectively explain only a few percent of total community variation [17,32]. Complementary population studies in relatively homogeneous environments found that overall gut microbiome composition is only weakly associated with genetic ancestry and is dominated by environmental and lifestyle factors such as diet, medications and cohabitation [33,34].

Beyond the microbiome itself, host genetics also play an important role in defining individual infectious disease risk. Systematic reviews across tuberculosis, influenza, RSV, SARS-CoV and pneumonia highlight consistent but heterogeneous genetic contributions to susceptibility and severity, often in classical immune genes including HLA, TLRs and cytokine receptors [35,36,37,38]. A similar host-conditioned model is plausible for microbiome-mediated conditions: even where bacteria are required for disease, they are not sufficient to determine who becomes clinically affected.

Clostridioides difficile infection (CDI) illustrates this point. CDI is initiated by antibiotic-induced dysbiosis, and microbiome restoration (especially faecal microbiome transplantation) is highly effective in many cases. Recent work highlights that recurrent CDI is strongly conditioned by host factors: impaired humoral responses to toxins A/B, specific immunogenetic backgrounds and dysregulated mucosal immunity markedly increase recurrence risk, while genetic variation modulates response [39,40,41]. Thus, even in a paradigmatically microbiome-driven condition, colonisation and dysbiosis are necessary but host context largely determines progression to recurrent disease.

5. Marital and Cohabiting Pairs as Natural Experiments in Microbiome Causality

Spousal and household designs offer a powerful but underutilised way to probe microbiome causality. Disease co-occurrence can be measured in spouses (the likelihood that a spouse develops the same chronic disease) and in families (the likelihood that a genetically related family member develops the disease). Comparing these patterns yields informative contrasts. Spousal exposure is non-genetic (at least in most societies), with substantial similarity of dietary and environmental exposures, lifestyle and close contact. All of these would favour a substantial degree of microbiome similarity, particularly later in life, under the assumption of partial or complete transmissivity of the microbiome. By contrast, family-based studies, especially sibling comparisons, reflect partial genetic sharing and deeper microbiome sharing that is concentrated in early life.

Empirical microbiome data align with this framework. Cohabiting couples and household members share more microbial taxa across gut, oral and skin sites than unrelated individuals, with especially strong cohabitation effects on the skin microbiome; notably, spouses share microbiomes not only with each other but also with their dogs [42]. Older adult couples show greater gut microbial similarity and higher diversity than individuals living alone, and the strongest convergence is seen in couples reporting very close relationships, even after adjusting for diet, BMI and comorbidities [12,43]. These observations indicate that sustained close contact and a shared environment foster microbiome convergence, but they do not by themselves distinguish whether the microbiome is a primary causal driver of shared disease risk or a downstream marker of joint lifestyle and host susceptibility.

This framework is especially informative in light of historical trends: many classic family-based studies were published in the 20th century, when divorce rates were lower and spousal similarity in environment and behaviours expectedly higher than today. More recent cohorts, in which assortative mating for education, BMI and health behaviour is well documented, suggest that part of spousal medical concordance reflects selection into similar lifestyles rather than purely shared exposures after marriage [44]. Spousal designs therefore test a composite of shared socioeconomic position, health behaviours and mid- to late-life environmental factors, whereas familial clustering reflects genetic and early-life factors, including early microbiome assembly [45].

Below, we summarise spousal concordance patterns across several disease domains and consider how compatible they are with a strongly transmittable versus a host-constrained microbiome. Notably, the inherent differences between published study designs, sample collection process, sequencing platforms used and various bioinformatics approaches may introduce a substantial degree of heterogeneity and cause spurious associations between implied microbiome and disease status.

5.1. Metabolic and Cardiometabolic Disorders

An overview of this field suggests significant spousal concordance for metabolic syndrome and cardiometabolic risk factors, with odds ratios typically in the 1.5–2.5 range for one partner being affected if the other is affected and substantial contribution from shared behaviours such as diet, smoking and physical activity [46,47,48,49,50]. A meta-analysis of spousal diabetes found that having a partner with type 2 diabetes confers roughly a 20–30% increase in diabetes risk, consistent with notable but far from deterministic shared risk [51,52]. More detailed physiological phenotyping in couples indicates that spousal similarity is stronger for upstream behavioural and adiposity traits (diet quality, physical inactivity, BMI and waist circumference) than for downstream glycaemic markers, supporting shared lifestyle and assortative mating as key drivers of cardiometabolic concordance [53].

The gut microbiome is robustly associated with obesity, insulin resistance and metabolic syndrome, and animal studies show that microbial transfer can modulate adiposity and glucose homeostasis. Microbiome GWAS and mediation analyses also suggest that some host loci affecting metabolic traits (e.g., lipid and bile acid metabolism) partly act via effects on specific microbial taxa or functions [16,17]. However, in human marital cohorts, spousal concordance in metabolic syndrome appears largely explained by shared behaviours and adiposity, with microbiome similarity likely co-travelling with these factors rather than acting as an independent first mover. In observational work linking relationship quality and the gut microbiome, older cohabiting couples with closer relationships had more diverse gut communities and fewer taxa previously associated with cardiometabolic disease, but these associations remained modest after accounting for health behaviours [12]. This pattern supports a model in which the microbiome modifies metabolic risk and reflects cumulative lifestyle and pharmacologic exposures, rather than being an autonomous, strongly transmittable cause for most individuals.

5.2. Oral Conditions

Oral pathogens such as Porphyromonas gingivalis and Streptococcus mutans can clearly be transmitted between family members and spouses. Clonal and fimA-type similarity studies show frequent sharing of P. gingivalis strains within couples, and systematic reviews support both horizontal (spouse-to-spouse) and vertical (parent-to-child) transmission of key periodontal organisms [54,55,56]. Horizontal transmission (between spouses) showed a substantial degree of variation, with an estimated 14–60% for Aggregatibacter actinomycetemcomitans and 30–75% for P. gingivalis [57]. Yet despite frequent microbial transmission, not all colonised spouses develop severe periodontitis or caries, and substantial interindividual variation persists after accounting for colonisation status [58,59,60]. In causal terms, the microbiome here seems to function as a necessary but not sufficient component: specific microbes are required for disease, but they are not the sole determinant of who becomes clinically affected. Notably, not all oral conditions seem to show such lack of spousal sharing. A recent study on the oral microbiome in newlyweds suggested that oral microbiome sharing coincided with symptoms of depression and anxiety, thus suggesting that microbiome colonisation may be partly implied in such findings [61].

5.3. Inflammatory Bowel Disease and Other Immune-Mediated Diseases

For Crohn’s disease and ulcerative colitis, profound dysbiosis is consistently observed, and microbiome-focused therapies (antibiotics, FMT and probiotics) have modest and often transient effects. Conjugal cases of inflammatory bowel disease (IBD) are rare, given disease prevalence and marriage rates. Early registry work in France and Belgium identified only 30 conjugal instances over large catchment populations; in most couples, disease onset occurred after years of cohabitation, and although conjugal clustering was slightly higher than expected by chance, the authors concluded that a shared environment alone was unlikely to explain IBD incidence [62]. A more recent systematic review and European survey identified just 58 couples with IBD; over 40% of spouses were diagnosed before cohabitation and another 12.5% had discordant timing, and the lifetime risk of IBD in their offspring, while elevated (~10–16% by early adulthood), remained well below what would be expected under a strongly transmittable, household-driven aetiology [63]. Notably, there are isolated studies that indeed invoke more common conjugal cases, attributing this finding to environmental factors or unknown external agents [62,64,65], with one such study invoking a possible infectious agent [66].

By contrast, a positive family history in a first-degree relative is consistently the strongest risk factor for incident IBD, with population-based cohorts showing several-fold elevation of risk in offspring and siblings and rising recurrence risk when multiple relatives are affected [67,68,69,70]. Genome-wide association studies implicate over 200 loci, many in innate and adaptive immune pathways, that collectively explain a substantial fraction of familial aggregation [71,72]. Against this background, the rarity of conjugal IBD and the much stronger clustering in genetically related family members support a primary role for host genetics and early-life environment, with microbiome perturbations acting within that predisposed context rather than as a transmissible household agent.

5.4. Neurodegenerative Diseases

Microbiome alterations are repeatedly reported across a spectrum of neurodegenerative diseases. Parkinson’s disease is associated with reduced short-chain fatty acid production, increased pro-inflammatory taxa and altered bile acid metabolism, and animal models suggest that microbial products can modulate α-synuclein aggregation and neuroinflammation [73,74,75,76]. However, spousal studies and case series have found either no excess over chance or very small numbers of affected couples. Detailed clinical and pathological work on conjugal pairs shows that spouses often have different PD subtypes or pathologies, suggesting that direct person-to-person transmission as a cause of the disease is less likely that environmental exposures in the form of heavy metals [77,78,79,80].

Similarly, conjugal multiple sclerosis (MS) is rare [81,82]. A population-based study in Canada estimated that, despite considerable ascertainment effort, a shared adult environment among spouses did not measurably increase MS risk, whereas recurrence risks in offspring of affected parents were markedly elevated, supporting a largely genetic and early-life environmental contribution [83,84]. Dementia shows somewhat stronger spousal concordance, albeit burdened by methodological limitations [85]. Subsequent registry-based and cohort studies report more modest hazard ratios for incident dementia or cognitive decline in spouses, most of which attenuate after adjustment for education, cardiovascular risk factors, depression and caregiving burden [86].

5.5. Longevity and Healthy Ageing

A few studies have reported distinctive microbiome patterns in centenarians and exceptionally healthy older adults, including enrichment of taxa involved in short-chain fatty acid production and bile acid metabolism and depletion of taxa associated with frailty and chronic inflammation [87,88,89,90]. These findings support a role for the late-life microbiome as a biomarker and potential modulator of resilience. However, demographic and genetic work indicate that human lifespan is only moderately heritable. Twin studies in Danish and other European cohorts estimate heritability of longevity at most in the 0.1–0.3 range, with the majority of variance attributable to non-shared environmental factors [91,92]. Analyses of millions of relatives in large online family trees show that once assortative mating is properly accounted for, genetic factors explain less than 10–15% of lifespan variance, and correlations in longevity between spouses are modest, reflecting a shared environment and lifestyle rather than shared genes [93,94].

Collectively, these patterns indicate that spouses do converge in microbiome composition and in many chronic disease risks, but the degree of shared disease risk is too small and too behaviourally mediated to be compatible with a strong, universal microbiome-driven causality across these conditions. In most domains, familial clustering that tracks genetic relatedness and early-life environment is stronger than conjugal clustering; within couples, concordance is highest for behaviours and upstream risk factors, and microbiome similarity tends to mirror those shared patterns. The shared contribution is likely attributable more strongly to shared lifestyle, assortative mating and host background risk, with the microbiome acting predominantly as a conditional mediator or biomarker within a host-defined, environmentally shaped system rather than as a broadly transmittable agent.

6. Future Directions

Several underused study designs could help further clarify when microbiome differences are closer to causal and when they primarily track upstream host and environmental factors. Relocation studies with pre- and post-exposure sampling, for example, when individuals move between rural and urban settings or between countries with different dietary patterns, could illuminate how rapidly and to what extent the environment reshapes the microbiome and disease risk. Similarly, longitudinal pre- and post-marital designs, following individuals as they enter cohabiting relationships, would allow direct estimation of how quickly spousal microbiome convergence occurs and how that relates to changes in shared behaviours and health outcomes. More generally, we need to expand current disease models by introducing repeated sampling rather than relying on single time points to better understand disease dynamics and to distinguish cause from consequence. High-resolution longitudinal cohorts that collect host genetic data, immune phenotyping, metabolomics and detailed lifestyle information alongside microbiome profiles will be particularly valuable.

Special study designs should be prioritised whenever possible to improve power for detecting weak but potentially important effects. Spousal and household studies are powerful for separating a shared environment and microbiome from host genetics. Designs that exploit within-family contrasts, such as discordant twin pairs or siblings, and within-household cross-over interventions, can further reduce confounding and sharpen causal inference.

Finally, molecular pathology focus and scope should be broadened. It is not enough to seek a simple association, researchers should delve into systems biology and medicine, which are encumbered by numerous analytic and methodological hurdles. Instead of simplicity, researchers should embrace complexity and seek to integrate various sources of data toward better health and disease understanding. This may include integrating tissue-level microbial features, such as Fusobacterium nucleatum in colorectal cancer [95], HPV in cervical lesions and EBV in gastric and nasopharyngeal carcinoma [96] with immune infiltrates and genomic alterations, or using molecular tumour boards to interpret microbiome data in the context of host and tumour biomarkers, not in isolation.

6.1. Implications for Interventions and Clinical Translation

Taken together, host genetic data, marital studies and disease-specific patterns suggest that, for most conditions, the microbiome behaves as constrained by host genetics, immune tone and physiology, shaped by cumulative lifestyle and environmental exposures (diet, physical activity, sleep, stress, pollution and medications), and it is reactive to disease processes rather than fully independent. Within a wider lifestyle medicine framework, many interventions such as Mediterranean-style diets, increased plant fibre intake, regular exercise, stress reduction and improved sleep can be viewed as allostatic “deloading”: they reduce systemic strain and inflammation while simultaneously shifting the microbiome towards more diverse, metabolically favourable communities [97,98]. The microbiome should therefore be considered as both a sensor and effector of improved health behaviours, but not their sole mediator.

Crucially, this implies that microbiome-only interventions (for example, probiotics or narrow-spectrum microbiome modulators) are unlikely to reverse complex chronic diseases in the absence of broader changes in host state and the environment. The benefit of microbiome-targeted interventions is therefore likely to be conditional: some individuals will be much more responsive than others. Identifying these subgroups, the host and environmental contexts in which microbiome modulation is most effective, is essential for rational clinical use.

In many settings, the microbiome may be most useful as a biomarker of allostatic load and host resilience, helping to stratify risk and monitor response to primarily host-targeted interventions. Study designs should routinely collect host genomics, epigenetics, metabolomics, immune phenotyping and detailed exposure histories alongside microbiome data, following the integrative template of HMP2 [3]. Rather than seeking universal microbiome biomarkers for broad disease categories, studies should focus on specific endotypes and contexts where the microbiome is plausibly closer to causal. Finally, microbiome interventions should be embedded in broader programmes that address diet, physical activity, sleep, stress and medication burden, recognising that these levers operate synergistically.

6.2. Limitations

This review has several limitations that should be acknowledged. First, it is a selective, narrative synthesis rather than a formal systematic review. We aimed to prioritise large, well-characterised cohorts, registry-based studies, twin and family designs and Mendelian randomisation analyses. However, we cannot exclude selection bias in the studies we chose to emphasise due to very diverse research study designs, approaches and analytic framework that prohibited a synthetic approach, like a systematic review or meta-analysis. Relevant publications may have been missed, and the balance of evidence presented inevitably reflects our judgment about which findings are most informative for the question of microbiome causality versus biomarker status.

Second, the spousal and household concordance data we draw on are heterogeneous in design, era and population. Many classic studies were conducted in older cohorts from high-income countries, often at times when divorce rates, smoking prevalence and diagnostic criteria differed substantially from today. Ascertainment and misclassification of disease, incomplete adjustment for socio-economic status and differences in follow-up duration can all influence estimates of spousal and familial risk. Moreover, assortative mating by education, lifestyle or latent health traits can inflate spousal concordance independently of any shared microbiome, whereas under-ascertainment of conjugal cases can lead to underestimation of shared environmental contributions. For rarer conditions such as multiple sclerosis, inflammatory bowel disease or parkinsonism, the number of conjugal pairs remains small, limiting statistical power to detect anything other than very large shared effects.

Third, microbiome studies themselves are subject to substantial methodological variability. Differences in sampling site, timing, storage, DNA extraction, sequencing platform and bioinformatic pipelines, as well as the use of 16S rRNA versus metagenomic approaches, can all alter measured community profiles and strain-level resolution. Most human data are cross-sectional or sparsely longitudinal, with relatively few studies incorporating repeated sampling over critical windows of disease onset or progression. These factors can attenuate or distort associations and complicate comparisons across studies, which means that the true magnitude of microbiome–disease relationships may be either underestimated or overestimated in the available literature.

Finally, our tripartite classification of microbiome roles into primary driver, conditional mediator or effect modifier and association biomarker is intentionally simplified. In reality, causal architecture is likely to be more complex, with bidirectional feedback between host and microbiome, stage-specific and tissue-specific effects and heterogeneous mechanisms across timeline and disease endotypes. Individual taxa or functional pathways may play different roles at different time points or in different genetic and environmental backgrounds.

7. Conclusions

The human microbiome has transformed our view of the body’s ecology, but the temptation to treat it as “the next genetics,” a singular, dominant explanatory framework, risks over-promise and misdirected interventions. Evidence from host genetic studies, marital and household concordance, clinical intervention trials and longevity research converges on a more nuanced conclusion. With several notable exceptions, the microbiome seems to act primarily as a conditional mediator or effect modifier whose significance depends on host genotype, immune tone and lifestyle. Rather than being a root cause of most complex diseases, it is better understood as a high-dimensional biomarker of upstream biology and allostatic load.

Recognising this does not diminish the importance of microbiome science; instead, it situates it appropriately within systems biology and lifestyle medicine. To realise its promise, we must move beyond simple case–control correlations toward holistic, causally informed and context-aware frameworks that explicitly integrate the host, environment and microbiome. Only then can we identify when microbiome modulation is truly a key therapeutic lever and when it is primarily a mirror reflecting deeper, modifiable aspects of human biology and pathology.