As has now been well documented, the promise of the self‐proclaimed ‘Fourth Generation' theoretical project never truly came to fruition, despite some highly impressive initial progress by its champions, and valiant later attempts to redeem it by subsequent scholars.

Its troubled history shows that Ritter and Beck are right to identify the pernicious qualities of generational thinking.

As I have written previously, it was “the coincidence of the idea of theoretical generations with the emergence of a self‐consciously ‘fourth' generation” that encouraged the “peculiar tendency towards introspection” that plagued the fourth generation project (Abrams, 2019, p. 381).

Such a tendency, as Ritter (2019:106) has noted, has prompted many scholars in our field to become “unnecessarily modest, anxious, and…too self‐limiting.” There are certainly benefits of thinking beyond generations, but doing so does not permit us to erase the fourth generation from existence.

If we are to seriously consider dispensing with the concept of generations we must distinguish between the hypothetical three generations of revolutionary theory which Goldstone popularised and the proposed fourth generation which he, Foran and Lawson consciously advanced.

Given the distinctive characteristic of the fourth generation of revolutionary theory as a self‐professed project— an “agenda to be fulfilled” rather than an historical interpretation (Abrams, 2019; Lawson, 2016, p. 110)— discarding it as the latter would be a category error with pernicious consequences.


Despite its initial promise, the influence which the fourth generation project exerted on the field of revolutionary theory eventually became – for numerous reasons (see: Abrams, 2019; Allinson, 2019; Beck, 2018; Lawson, 2016; Ritter, 2019) – a limiting one.

There are lessons to be learned from the fixation of a whole body of scholars on conjunctural modelling and ontological discourses intertwined with the progressive decrease in scholarly rigour observed by analyses of work in the field (Beck, 2017).

Worse still, as Beck (forthcoming) has noted, it seems that “what we think we already know about revolution may very well be probably wrong.” Amid all the problems we have encountered over the years, should we decline to consciously break with the conditions which cultivated them we risk doing more to preserve our field's current impasse than to move past it.

We risk allowing the much decried maladies of the fourth generation project to become embedded in the complex web of scholarship that constitutes work on the subject of revolution.

It is for this reason I would dissent from the suggestion that that the future of revolutionary theory demands that we “free ourselves from wondering what the fourth generation was or is, and what the fifth generation is or will be,” (Beck & Ritter, 2021) Rather, reflection on where progress has tended to stall and how we might do better is vital to the renewal of the field.

Refusing to acknowledge the fourth generation project's initial promise, latter faltering, and eventual decline not only obscures the great deal of intellectual effort poured into its ultimately unsuccessful project, but also renders critical reflection on its scholarly agenda all the more difficult.

This, in turn, might allow certain extant ideas associated with the fourth generation to remain as hegemonic than they have been in the past, yet also insulate them from critique.

All this might leave us “profoundly in the thrall” of past perspectives – as Eric Selbin (1999, p. 10) once decried of fourth generation theories.


Elsewhere, Beck (forthcoming) has rightly noted that “thinking of prior theory in generational terms has overstated the extent of consensus present at any time.

” The same accusation can be made against Ritter and Beck's claim about the extent of consensus present in our field today.

While Ritter and Beck seem content to break with the practice of generational thinking, they nonetheless appear to present some of its symptoms by depicting as ‘consensus' what are in fact still quite clearly matters of scholarly difference.

This arises with respect to two crucial areas of revolutionary theory: the object of study, and method of inquiry.

Truly thinking beyond generations necessitates that we reject the notion that we have settled certain major debates in revolutionary theory and now need only to perfect our knowledge of revolution. Should we rid ourselves of generational language, but retain the biases of generational thinking – that certain matters are now off the table, and that our field has a single renewed purpose –we risk being trapped ‘beyond and within’ the fourth generation of revolutionary theory, having formally eschewed its aesthetic trappings, but unable to escape its more substantive shortcomings.


It is tempting to think that theorists of revolution generally agree on what exactly it is that they are studying.

In line with such a view, Ritter and Beck seek to claim that revolutionary theory's “object of study is clear to contemporary scholars.

” They argue that revolutionary theorists are united in studying “attempted and successful shifts in, at the very least, regimes or political structures that occur through mass mobilization.

” This is far from a consensual or established definition.

Not only would it exclude, for example, much of the history of the French Revolutionary period (a classic case in our field), but also call into question the study of central questions in revolutionary theory such as ideology, culture, micro‐mobilization and elite agency.

This is not even to mention currently popular areas of focus such as revolutionary preconditions, regime collapse, revolutionary and counterrevolutionary outcomes (Allinson, 2019).

Conversely, such a definition of revolution might puzzlingly include cases like the Obama‐era Tea Party movement in the United States, which utilized mass mobilization in order to shift political structures (and arguably eventually the governing regime).

The US Civil Rights Movement also substantially shifted political structures through mass mobilization, and – depending on how one defines a regime ‐ one could even imagine a grassroots election campaign constituting an appropriate object of study as per Ritter and Beck's definition of revolution.

There is a substantial variety of definitions of revolution that differ widely from the one Ritter and Beck argue is ‘clear’ to contemporary scholars.

One such scholar, George Lawson (2019:10), considers a revolution to constitute “a collective mobilization that attempts to quickly and forcibly overthrow an existing regime in order to transform political, economic, and symbolic relations.

” This definition shifts the onus from mass mobilization to merely collective ones, but also introduces new criteria such as speed and force, as well as economic and symbolic transformations.

By contrast, Selbin (2008:131) considers revolution to be a “conscious effort by a broad based, popularly mobilized group of actors, formal or informal, to profoundly transform the social, political, and economic institutions which dominate their lives; the goal is the fundamental transformation of the material and ideological conditions of their everyday lives.

” This definition introduces ideas of intention, the source and breadth of mobilization, and the extensive transformation of various institutions as well as of material and economic conditions.

Yet other scholars – such as John Dunn (2018) – have even contended that revolutions no longer occur, arguing that they are defined by a transformative ideology that present day cases lack.

This line of contention has led to substantial recent disagreement over whether to define revolution as a political process at all, or whether a programmatic definition is preferable (Abrams, 2018; Abrams & Dunn, 2017; Ritter, 2019; Slim, 2018).

Such is the degree of difference over revolution as an object of study that Ritter and Beck's proposed consensus definition also breaks with ones they each have offered very recently.

Ritter (2019) has recently defined revolutions as “simply an irregular overthrow of a political regime through mass mobilization.

” Ritter thus excludes ‘political structures’ and ‘attempts’ from his definition.

Beck (2016:17) has previously defined revolutions as “mass contention against an existing state [which] overthrows a regime, or creates lasting social change and establishes new political structures.

” This is also a narrower object of study than the pair's joint definition: it specifies revolution as a state‐centric phenomenon, and specifies that – in the absence of regime overthrow ‐ durable social change should occur and novel political structures should be created rather than only shifted.

For my own part, I consider a revolution to constitute a popular overthrow of the power structures in an existing polity.

These can of course be stipulated as successful, failed, ongoing, or even imagined.

In some regards this definition is stricter than Ritter and Beck's: the US Civil Rights Movement – which notably altered, but did not wholly overthrow the power structure in the United States – would not be counted as a revolution.

In other regards, this is a much more expansive definition: it unchains revolution from the state or regime, and includes in its scope the popular overthrow of power structures on a small scale (e.g. a workplace, locality, or institution) and a very large scale (e.g. the international order).

All this is suffice to say that our object of study is still subject to a plurality of interpretations.


Just as it is tempting to think that revolutionary theory no longer harbours disagreement about its object of study, it is equally attractive to think that “debates over method are… of a time that is passed.” ( Beck & Ritter, 2021).

Beck and Ritter argue that the solution to these debates is to be found in “a pluralistic methodology” that tends to (though might not always) combine quantitative and qualitative methods for optimal results.

Such a line of contention was prominently advanced in Goldstone's Toward a Fourth Generation of Revolutionary Theory (2001:175), which concluded that fourth generation studies would “unify the results” of a variety of qualitative and quantitative methods.

Just as Ritter and Beck contend that “we should not expect one subfield to solve the structure‐agency problematic,” it is similarly unrealistic to suggest that revolutionary theory has solved the methodological one.

Indeed, Beck (2017:533) himself has observed “a lack of consensus… and little convergence over time,” over issues such as case selection and research strategy.

Elsewhere, he has remarked that the study of revolution exhibits “no convergence on the study design techniques that comparative methodologists recommend.

” ( Beck, forthcoming).

The mixed method studies to which Ritter and Beck assign preference are not necessarily superior to single‐method modes of inquiry.

Indeed, though they sometimes offer substantial benefits, mixed method approaches are at other times inappropriate, impractical, illogical, or unnecessary for attacking some of the most interesting research puzzles which we come across (Halcolmb, 2018; Loo & Alan, 2011; Symonds & Gorard, 2010; Timans, et al., 2019).

Though analyses which bridge quantitative and qualitative approaches may indeed sometimes yield “the advantage of both,” there are also occasions in which a slapdash historical survey or a surface‐level suite of descriptive statistics does little to enrich scholarly analysis, and yet efforts to produce them might still take up vital portions of an article's word count, or cut‐short more enriching research pursuits.

Conversely, there are also many cases in which profitable mixing of methods occurs strictly within either the qualitative or quantitative methodological silos.

On still other occasions, there are times where single‐method studies yield new empirical or theoretical discoveries that were attained precisely because of the practitioner's commitment to honing and furthering their chosen method.

Given the surprising methodological poverty in the study of revolution (Beck, forthcoming) it is particularly important that we should prioritise that methods be well executed, rather than that they be combined.

There are many opportunities available to revolutionary theory should we harness a greater number of methods than we have in the past, but this is not simply achieved by harnessing past methods simultaneously.

Instead, we should be encouraging a greater variety of methods – be they singular or mixed– in order to further our understanding of revolution's multiple dimensions.

Sometimes, this will take the form of Ritter and Beck's proffered “pluralistic methodology,” but we must not allow excitement about such approaches to obstruct a broader pluralism about methodology.

Only by paying attention to scholars with diverse and disruptive methods, as well as experts in established methods and mixed‐methods analysts will we open the field for new and challenging discoveries.


Beck and Ritter are right to argue that we must pay careful attention to the “continuity of debates over the decades.” However, we cannot simply regard these debates as settled merely because scholars no longer disagree as violently as we imagine they might have in the past.

In my view, it is not so much that past debates have been settled in favour of a clear or consensual answer – as Beck and Ritter contend ‐ but rather that other scholarly fashions rose to prominence.

There are persisting differences regarding our object of study or how we might study it, and the empirical variety of revolutions and ways to study them have only increased.

It is precisely by means of this increase in cases and studies that we have seen that there is a great deal more to talk about than the hypothetical perfection of definitions or methods.

While there are many compelling reasons to move past the classification of revolutionary theory into neatly organized ‘generations,’ a conscious and critical acknowledgement of the fourth generation project is nonetheless necessary.

This is because of its status as a specific intellectual project, and its prominent role in many scholars' theoretical education.

This role has not always been productive.

As Ritter and Beck astutely note, the ‘generational thinking’

which accompanied the fourth generation's rise to scholarly prominence obscured valuable work, made invisible certain processes of knowledge accumulation, and is at this point limiting our theoretical imagination.

Just as they note that this mode of thinking cultivated a “scholarly fear” of returning to past insights, we should not exhibit that same fear with regard to revisiting past debates.

Beck and Ritter are right to critique the generational framework used to chart the history of revolutionary theory, but dispensing with this notion does not mean that the fourth generation project should be given a free pass.

When Beck and Ritter condemn generational thinking as a false premise, they condemn the foundational premise of the fourth generation project as equally false.

Nowhere has the generational framework been more celebrated than in the work of self‐professed fourth generation scholars, and the generational teleology of revolutionary theory is arguably more vital to ‘Fourth Generation’ accounts than any other attribute.

It is precisely because fourth generation theory served as a backdrop for the obfuscation of scholarly history – rightly bemoaned by Ritter and Beck‐ that we should welcome a critical evaluation of its ideas, and open discussion of where we might go next.


Ask any rising scholar in the study of revolution, and they will agree that our field finds itself in a pivotal moment.

Allinson (2019) has argued that existing theories of revolution are failing to keep pace with empirical developments.

Ritter (2019:106) and Beck (forthcoming) have gone even further, declaring the death and rebirth of our past approaches to studying revolution: “The Revolution is dead, but long live the revolution,” as Ritter puts it; “Revolution studies is dead; long live revolution studies,” as Beck does.

But once we break with the thinking of the past and its generational imagery, what – as the old revolutionary question goes – is to be done? How might we reopen our minds after decades of generational thinking and actively work to regenerate revolutionary theory? With regard to our hopes for the future of revolutionary theory, there is much that Allinson, Beck, Ritter and I have in common.

All of us call for a break with the past.

Whether we call what comes next a new ‘generation’ or simply “a new future for the study of revolution,” as preferred by Ritter and Beck, we all hope that it will be characterised by an influx of novelty, be it new theories, cases, phenomena, or interpretations.

There are still more valuable sources of novelty in addition to those imagined by Ritter and Beck.

We might also – for example ‐ hope to see the arrival of novel research methods, ethical standpoints and practical tools.

We might seek dialogue with new fields or reinvigorate our relationships with those we now tend to ignore.

Regenerating revolutionary theory will require more than novelty alone.

It is not enough to simply resume existing debates on the basis of uncritically inherited premises.

We can and must go one step further and reopen old conversations, question received knowledge, and initiate new debates altogether.

This means that we should not exclusively align the advancement of our field within the constraints of a rigid and immovable object of study, nor amid the strictures of methodological orthodoxy.

Instead, we should make room for disruptive methods, challenging epistemological positions, dialogue with practitioners, and research from new or unexpected sources.

Casting aside generational thinking (and with it the strictures of the fourth generation project) may well be fruitless if we do not think carefully about where our field has gone astray, and how we might pursue more constructive directions.

We risk the degeneration of revolutionary theory into either a false narrative of consensus or isolated siloes of study with disconnected debates and literatures.

Thus, in the years to come scholars should consciously pursue the deliberate regeneration of our field: building connections between debates and fields, educating new scholars, casting aside honorific hierarchies, and celebrating disagreement and diversity.

If we are to advance the field of revolutionary theory we need to explore in multiple directions, lest we find ourselves on yet another winding path or risk making a wrong turn.

Only an open field of study can provide insurance against such a fate.

To join in what is a fast emerging scholarly trend: the fourth generation of revolutionary theory is dead, long live the regeneration of revolutionary theory.


The incidence of type 1 diabetes in many developed countries has been increasing at rates faster than can be explained by the known genetic propensity towards the disease [1].

The envi- ronmental triggers of this disease have not yet been identified, despite the many efforts to associate diet, vitamin D, viruses and other factors with disease [1].

A leaky gut has been cor- related with type 1 diabetes [2], and an aberrant gut microbiome was proposed as the factor that results in a leaky gut followed by altered immune responses leading to disease [3].

This review describes a model for the role of the gut microbiome in type 1 diabetes based on the latest results.


Past work with non-obese diabetic (NOD) mice and BioBreeding diabetes-prone (BB-DP) rats provided the first Microbiology and Cell Science Department, Institute of Food and Agricultural Sciences, 1355 Museum Road, PO Box 110700, Gainesville, FL 32611-0700, USA Diabetologia (2015) 58:1386-1393 DOI 10.1007/s00125-015-3614-8 evidence that bacteria may play an important role in the onset of type 1 diabetes.

BB-DP rats fed sulfamethoxazole, trimeth- oprim and colistin sulphate demonstrate a significantly de- creased incidence of diabetes compared with controls [4].

A similar increase in disease-free animals vs controls was ob- served in NOD mice after administration of doxycycline or vancomycin [5, 6].

Bacteroides were higher in stools from the BB-DP rat while Lactobacillus was higher in the diabetes- resistant rat (BB-DR) [7].

Strains of Lactobacillus johnsonii and Lactobacillus reuteri isolated from BB-DP rats prevented and promoted diabetes in BB-DP rats, respectively [8].

The L. johnsonii strain induces a T helper 17 cell bias in the mes- enteric lymph nodes of C57BL/6 mice while the L. reuteri strain does not [9].


Although the incidence of insulitis is not significantly dif- ferent between germ-free and specific-pathogen-free (SPF) NOD mice, germ-free mice develop insulitis earlier than SPF mice [10, 11].

The presence of segmented filamen- tous bacteria (SFB) in female NOD mice is correlated with decreased insulitis incidence and with the percent- age of IL-17-expressing lymphocytes [12].


Diet is known to significantly affect diabetes incidence in NOD mice.

For example, a high-cereal diet increases diabetes incidence in NOD mice, while a high-protein diet reduces risk [13].

Recent studies observed that acidic drinking water also increases diabetes incidence in NOD mice, and this is prevented by inoculating mice with SFB [14].


The results of the murine experiments encouraged efforts to determine whether associations between gut bacteria and type 1 diabetes could be discovered in humans.

Rodent model in- vestigations suggested that healthy children may have high populations of probiotic-like bacteria such as Lactobacillus, while the gut microbiome of unhealthy children may be dom- inated by Bacteroides.

Perhaps antibiotic or probiotic use ear- ly in life could prevent type 1 diabetes, but this requires more study.

Analysis of a Finnish dataset revealed no connection between antibiotic use and autoimmunity for type 1 diabetes [15], but larger cohorts may be needed to observe an antibiotic influence on this disease.

Early on, a simple picture was ex- pected to emerge by simply studying the stool content of a few human cohorts with individuals at high genetic risk of type 1 diabetes.


At first, with very small human cohorts with only four healthy children and four children with type 1 diabetes auto- antibodies from the Diabetes Prediction and Prevention (DIPP) study in Finland, the human situation did appear to be very similar to the murine one [16, 17].

These studies showed highly significant taxonomic and functional differ- ences between cases and controls prior to autoimmunity for type 1 diabetes.

Levels of Bacteroides were higher in children with autoimmunity, and levels of seemingly protective unclas- sified Firmicutes were higher in healthy children.

These re- sults match the findings of the murine experiments except for the fact that the human samples had negligible amounts of Lactobacillus.


Contrasting results were obtained from an investigation of 298 stool samples collected during the first 3 years of age from 22 case and 22 control children enrolled in the German BABYDIET study [18].

Unlike the small Finnish cohort, no significant differences between taxa were observed between cases and controls after correction for false discovery rate.

Instead, network analysis showed that bacterial communities in cases were far less strongly correlated with each other at <6 months of age and again at about 2 years of age than in controls.

However, the results from the Finnish and German cohorts were similar in that the autoimmune bacterial commu- nities were less stable than the healthy communities.

The cause for this instability in autoimmune samples remains unknown.


A still larger set of 947 stool samples collected during the DIPP study from 29 cases and 47 controls during the first 4 years of life was examined [15].

All of the children enrolled in the DIPP study are genetically at high risk for type 1 dia- betes.

Analysis of these samples revealed a very striking re- sult: approximately 8 months prior to the average time of appearance of the first autoantibody in cases, a large increase in one bacterial species, Bacteroides dorei, was observed.

In addition, B. dorei was by far the most abundant species in autoimmune prone children, with over 20% of the population represented by this one species-a rate that was more than double the relative abundance of this species in healthy chil- dren.

This difference was highly statistically significant even after correcting for false discovery rate and was found to occur at about the same time as the introduction of solid food.

A few other minor taxonomic differences were also observed, but nothing as striking as the B. dorei result.


So why was the B. dorei result seen in the large Finnish DIPP cohort but not in the German BABYDIET study? At first glance, it may appear that since the B. dorei result in Finland was not reproduced in Germany, it must then be an anomaly.

However, there is one very important difference be- tween the designs of these two cohorts that may explain this difference.

The samples used in the DIPP study were all from children born in the same hospital, Turku University Hospital [15].

All of the children lived within 80 km of each other.

Thus, many of the known confounders of the microbiome community were controlled; that is, climate, diet, culture, wa- ter supply, pollution levels, air quality and medical practices were all very similar for these children.


In contrast, the children in the BABYDIET cohort were from all over Germany, allowing enough variability in the microbiome confounders to affect microbiome composition.

In addition, all of the children in the BABYDIET study had Diabetologia (2015) 58:1386-1393 1387 first-degree relatives with type 1 diabetes, while in DIPP the children were chosen for the study based on their high-risk HLA genotype at birth.

These confounders add to the noise of the data and may explain why no significant taxonomic dif- ferences were observed between cases and controls.

These confounders may have been sufficient to mask any taxonomic differences that might otherwise have been detected in the BABYDIET study but too weak to mask the network differ- ences seen in this cohort [18].


Hence, a working hypothesis is that geography plays a very large role in whether it is possible to observe significant case- control differences in any cohort designed to examine associ- ations between the microbiome and disease.

From this per- spective, the microbiome results from the BABYDIET and DIPP cohorts do not contradict each other but rather help us understand how best to design cohorts for future disease- related microbiome studies.


Two other recent studies from Europe and Mexico showed taxonomic differences in the gut microbiome between individ- uals with type 1 diabetes and individuals without the disease [19, 20].

In both studies, levels of Bacteroides were signifi- cantly higher in those children with type 1 diabetes compared with healthy children.

These studies were intended to show the effect of the disease itself as opposed to identifying a trigger for autoimmunity as in the previous studies.

The Mexican study was restricted to Sonora state, which borders Arizona, and was limited to 29 children, including eight con- trols.

Based on the findings of these studies and the pre- autoimmunity cohorts described above, Bacteroides appears to be a major contributor to microbiome dysbiosis both prior to the development of autoimmunity and after disease diagnosis.


There is evidence that the human genome has some control over the taxonomic composition of bacteria in the gut.

However, it is not known whether a genetic propensity to type 1 diabetes, as manifested by HLA genotype, affects bacterial gut composition [21].

The studies conducted to date on the relationship between type 1 diabetes autoimmunity and the microbiome have not seen an HLA genotype effect on the microbiome, but these studies did not have enough participants to observe such an effect [15, 18].


The hygiene hypothesis suggests that a lack of expo- sure to microbes early in life under hygiene conditions in the developed world contributes to a weakened im- mune system incapable of warding off the effects of detrimental bacteria in the gut [22].

Efforts are under- way to examine this issue carefully between Finland and neighbouring Estonia and Russian Karelia [22].

Testing this hypothesis will be difficult in any study protocol.

Our view that separating signal from noise is best done by careful cohort design intended to reduce the confounders of the microbiome.


Our recent results suggest that striking case-control differ- ences can be observed when the geographic distribution of a high-risk cohort is limited to one city and its immediate sur- roundings.

Another issue is whether type 1 diabetes risk is higher in urban or rural areas [23].

Differences in large gut microbiome taxa and alpha diversity differences were recently observed across six clinical sites in children at high genetic risk for type 1 diabetes [24].

Higher socioeconomic status is typically associated with type 1 diabetes and other autoim- mune diseases [25].

To date there are no reports in the litera- ture on socioeconomic status and gut microbiome composi- tion, but socioeconomic status has been shown to be associat- ed with the oral microbiome [26].

However, given that geog- raphy and diet are well known to affect gut microbiome com- position and that socioeconomic status affects diet and place of residence, cohort design should take these factors into ac- count or in some way control for them.

To date, no clear associations between type 1 diabetes and diet have been made, but this is being carefully examined by The Environmental Determinants of Diabetes in the Young (TEDDY) study group [27].


An ideal cohort design might be one that includes samples from a set of cities similar to Turku where the cohort includes children all born in the same hospital and all living in the surrounding area.

The variability of other environmental fac- tors, such as climate, drinking water, health practices and air quality, are similar across a single city.

Having a set of such cities each with children at high risk might allow us to identify bacteria or bacterial functions that are associated with autoim- munity for type 1 diabetes across a broad geographic scale.


Sampling from families may be a means of reducing the noise in the microbiome data.

That is, rather than collecting samples from healthy controls matched for genetic risk, con- trol samples could be obtained from healthy siblings.

But age matching is important, so noise could be further controlled by enrolling identical and non-identical twins for the study of those at high genetic risk for the disease at birth.

This would be a difficult undertaking in a small city such as Turku but might work well in a much larger city in the same country, such as Helsinki.


Another way of reducing noise would be to determine the changes that can occur in the gut microbiome communities in stools in response to long-term exposure to room temperature and air.

Approaches must be developed that make it easier for families to collect stools in a manner that allows for rapid freezing.

Microbial communities in stools do change signifi- cantly after 24 h of exposure to air, and these changes include a sharp decline in the relative abundance of Bacteroides strains [28], which can survive air but do not divide under aerobic conditions.

To date, none of the studies to investigate 1388 Diabetologia (2015) 58:1386-1393 the microbiome of children at high genetic risk for type 1 diabetes has been designed with the microbiome in mind.

In addition to the aforementioned improvements in study design and larger cohorts with limited confounding, advancements in big data analysis methods should also help to facilitate within- and between-study analyses.


As described above, a recent study showed a strong associa- tion between high levels of B. dorei in the gut several months prior to the appearance of the first type 1 diabetes autoanti- body [15].

A first step towards determining whether B. dorei causes type 1 diabetes autoimmunity would be to culture it from the Turku DIPP samples, then inoculate NOD mice with this organism and determine whether this strain(s) reduces the survival rates of the mice.

Although this is a high-risk strategy and not without significant intellectual challenges, it would be worthwhile to conduct such an experiment because even though B. dorei may not be involved in type 1 diabetes onset in other populations, a certain set of functions expressed by B. dorei may have wide applicability to autoimmune disease around the globe.


As more bacterial metagenomes are obtained from stool samples of high-risk children, quantifying the relative abun- dance of homologues or paralogues of B. dorei genes will be an easy task.

Additional Bacteroides species may be associat- ed with type 1 diabetes autoimmunity in other cohorts, which will allow us to build a database of genes in common with all Bacteroides associated with diseases vs those genes in Bacteroides species not associated with disease.

A similar approach can be used on bacterial species or strains thought to be protective against the disease.

Bacterial functions asso- ciated with disease have been listed for a very small cohort from the DIPP study [16].

Further studies are warranted on an increased number of individuals and more samples across ad- ditional time points.


As Bacteroides have been associated with type 1 diabetes autoantibodies, the next obvious question pertains to the source of these bacteria.

That is, if a single species or set of closely related species is associated with type 1 diabetes, the need to hunt for more bacterial associations with type 1 dia- betes declines and the problem becomes one of searching for those environmental factors that increase the abundance of these Bacteroides species in the gut.

In the stool samples of autoimmune Turku children, levels of B. dorei peak at about 7 months of age, about 1 month prior to the Finnish national standard for the timeframe of solid food introduction [15].

If the early introduction of solid food is related to raised Bacteroides levels, which foods are known to increase Bacteroides abundance in the gut?


High-fat and high-gluten diets encourage colonisation of the gut by Bacteroides.

Bacteroides populations in the human gut have been strongly correlated with a diet high in protein, animal fat, cholesterol and other fats [29].

Bacteroides are well known as the primary proteolytic bacteria in the gut [30].

Not surprisingly, vegans and vegetarians have low levels of Bacteroides in their stools [31].

To date, connections between a vegetarian diet and the progression towards type 1 diabetes have not been studied.


Examination of the gut microbiome in coeliac disease sug- gests that gluten may play a role in Bacteroides abundance.

The prevalence of B. dorei was 67% higher in children with active coeliac disease compared with the same children on a gluten-free diet for two years [32].

Similarly, formula-fed chil- dren at high risk for coeliac disease were approximately six- times more likely to possess B. dorei in stool than low-risk controls [33].

These results encourage further work to deter- mine whether gluten increases population growth of B. dorei in children at high risk for autoimmunity.

Resistant starch may also play a significant role in preventing type 1 diabetes as it has been reported to increase faecal butyrate levels [34].


The onset of type 1 diabetes might involve an aberrant intes- tinal microbiome creating the conditions for gut leakiness followed by mucosal immune responses that lead to an attack of insulin-producing islet cells [3].

Consistent with this notion, type 1 diabetic individuals have been shown to have a leakier gut than controls [35-37], and it has been reported that this leakiness is not caused by type 1 diabetes, rather, type 1 dia- betes is preceded by a leaky gut [2].

In the NOD mouse model, a leaky gut can lead to the activation of diabetogenic CD8 + cells which, in turn, lead to the development of insulitis [38].

Mechanisms for a connection between the leaky gut and type 1 diabetes have been reviewed previously [39, 40].


Butyrate is one of the short-chain fatty acids (SCFAs) pro- duced by bacterial fermentation in the gut.

The ratio of butyrate:acetate:proprionate in the gut varies widely, but these three SCFAs make up the majority of the SCFA content in this organ.

The concentration of SCFAs in the gut can be as high as Diabetologia (2015) 58:1386-1393 1389 140 mmol/l and these SCFAs can provide up to 10% of the daily human caloric needs [41].

Butyrate reduces trans- membrane transport of Escherichia coli across confluent monolayers of colon-derived cell lines [42].

In addition, in- flammation may reduce butyrate transport through the down- regulation of a butyrate transporter [43].


Butyrate is also known to play two important roles in maintaining the integrity of the epithelial layer.

First, butyrate induces the expression of mucin-producing genes in the epithelial layer [44].

Mucin is a host gly- coprotein that protects the epithelial layer from attack by bacteria or bacterial metabolites.

Second, butyrate restores and maintains tight junction assembly in epithe- lial layers [45, 46].

These tight junctions are formed between epithelial cells and serve as gatekeepers for metabolite transport across the epithelium.

Other SCFAs do not induce tight junction formation or mucin synthesis.


Healthy children have a higher proportion of butyrate- producing gut bacteria in their microbiomes compared with children expressing at least one beta islet cell autoantibody [16, 47].

Based on this, the ratio of butyrate-producing to other SCFA-producing bacteria in the gut was proposed to be a key regulator in determining the gut health of a child at high risk for type 1 diabetes [16].

However, there is no evidence to date linking butyrate levels in the gut to the progression of diabetes in either animals or humans.


Butyrate has roles other than maintenance of the integrity of the gut epithelial layer.

In particular, there is increasing evi- dence that butyrate has epigenetic effects that may be very important in type 1 diabetes, as unknown environmental trig- gers must be playing a role in the rapid increase in the inci- dence of this disease [48].


Butyrate induces the methylation of promoter regions, which causes both up- and downregulation in different sets of human genes [49].

Histone acetylation also appears to be regulated by butyrate production.

Butyrate reduces lipopolysaccharide-induced inflammation in the intestine through modulation of antioxidant defence systems [50], nitric oxide production, and expression of inflammatory cytokines [51].

Many of these effects appear to be modulated by the inhibition of histone deacetylases in macrophages [51].

Also, histone acetylation in the promoter of the Foxp3 locus Beginning with the introduction of solid food, consume a diet predominant in:


The genomes of two of the B. dorei strains from the DIPP Turku cohort have been sequenced to completion and, physi- ologically, appear to be typical Bacteroides in that they metabolise a broad spectrum of compounds and have many antibiotic-resistance genes [54].

One of these genomes was derived from a child who later developed type 1 diabetes- associated autoantibodies (case) while the other genome was from a high-risk healthy child (control).

The case genome in- cludes a bacteriophage with a DNA adenine methyltransferase gene that appears to be responsible for the methylation of all but three of the 20,554 GATC motifs in the genome [54].

Such phage-borne DNA methyltransferases have been found in sev- eral bacteria [55].

None of the nearly 19,000 GATC motifs in the control genome were methylated, presumably because it lacked this phage and its orphan DNA methyltransferase gene.

This work was the first report of DNA methylation differences within a bacterial species in the microbiome [54].


Although these methylation results from two B. dorei ge- nomes cannot be associated with disease, they suggest that a wider examination of methylation patterns in more samples is warranted.

DNA methylation patterns are known to affect gene expression in bacteria [56].

They control epigenetic re- sponses that are often regulated by environmental exposure.

Indeed, a few methylation sites in the human genome have been associated with type 1 diabetes pathogenesis [57, 58].

It is therefore conceivable that methylation differences in the genomes of bacteria associated with type 1 diabetes may play a role in disease.


The analyses described above suggest a revised model for the role of the microbiome in type 1 diabetes, one that is based on a previous model [16] but takes into account the role of diet (Fig. 1).

In this revised model, type 1 diabetes autoimmunity occurs when a high-fat, high-gluten diet, perhaps one with a low level of resistant starch, increases Bacteroides colonisa- tion in the gut.

These bacteria ferment glucose to SCFAs other than butyrate.

The lack of butyrate production reduces the likelihood of an intact gut epithelial layer.

The resulting per- meability causes bacterial antigen exposure that induces im- mune responses leading to type 1 diabetes autoimmunity.


Recent results suggest that the many confounders of gut microbiome composition need to be considered in cohort de- sign for the optimal discovery of associations between the gut microbiome and disease.

A cohort carefully designed to con- trol for geography and diet as much as possible can eliminate much of the noise that is often associated with microbiome data.

Future work will require experiments or observational studies that directly test each point in the model (Fig. 1).


Environmental factors play a role in the development of type 1 diabetes.

There is growing enthusiasm towards assessing the role of gut microbiota as an environmental modifier of autoimmunity in type 1 diabetes.

This is largely based on the increasing awareness of the role of microbiota in regulating gut homeostasis and immune responses in the gut [1].

Microbiota composition varies between individuals and is modified by diet [2].

An emerging number of studies suggest that the abundance of Bacteroidetes is associated with autoimmunity in type 1 diabetes [3].

Thus, a diet promoting an abundance of Bacteroidetes could be an environmental risk modifier in type 1 diabetes.


The notion of Bacteroides as ‘diabetogenic’ species is now further supported by a recent study in which Bacteroides dorei was found to be abundant in children who subsequently developed diabetes-related autoantibodies in a cohort of 76 children participating in the Diabetes Intervention and Prevention Project (DIPP) study in Southwest Finland [4].

In NOD mice, diabetes incidence is effectively modified by diet [5].

We have studied the effect of two common nutritional fibres, pectin and xylan, on microbial colonisation, gut homeostasis and diabetes development in NOD mice [6].

Pectin and xylan dramatically increased colonisation of the intestine by Bacteroidetes, including B. dorei (Fig. 1 and data in [6]), increased the expression of stress-related molecular transcripts and inflammatory cytokines in the gut, and altered the balance of T cell cytokines in pancreatic lymph nodes.

In terms of disease-promoting mechanisms, our findings alluded to a pathophysiological model [6] well aligned with the model recently discussed by Davis-Richardson and Triplett in Diabetologia [3].

The core idea is that, by displacing beneficial butyrate-producing taxa, Bacteroidetes have adverse effects on epithelial cells [3, 6].

Our findings provided experimental evidence in support of this and indicated that at least some Bacteroides species play a role in diabetes-related autoimmunity.

In addition, our results identified dietary fibres as novel candidates for environmental modifiers of the pathogenesis of diabetes.


Interestingly, induction of low-grade inflammation in the gut and microbiota encroachment was recently reported in mice following administration of two other fibre compounds used as dietary emulsifiers [7].

According to Finnish healthcare recommendations, fermentable fibres including pectin and xylan (hemicellulose, a constituent of all plant cell walls) are introduced into the diet of infants in the form of berry cocktails.

In Finland, parents are recommended to start giving berry cocktails to their infants at 4 months age, an age preceding the peak in the observed abundance of B. dorei [3].


Bacteroidetes are capable of adapting to different nutritional environments and they possess a large number of genes that encode carbohydrate-active enzymes.

After weaning, in the murine intestine, Bacteroidetes start to express a plethora of enzymes that participate in the liberation of simple sugars from complex polysaccharides of dietary fibres [8].

Simultaneously, the expression of genetic loci encoding enzymes involved in the synthesis of capsular polysaccharides is modified [8].

The immune-stimulating and metabolic effects of different members of the microbiota are still incompletely understood, and differences are likely between different individuals’ bacteria representing even the same species, including B. dorei [3].

We envision that fermentable fibres may modify the function and immunogenicity of microbiota, including members of the Bacteroidetes phylum, in ways which may have relevance to type 1 diabetes [6].


To the Editor: We agree wholeheartedly with Hänninen and Toivonen [1] that we must move beyond taxonomic associations of bacteria with type 1 diabetes.

However, a strong bacterial taxonomic association with disease, such as with Bacteroides dorei and type 1 diabetes autoimmunity in Turku, Finland [2], can provide a considerable number of clues about how the functions of this bacterium can lead to disease.

When a bacterial species is known at the genus and species levels and associated with disease, this allows us to make many predictions about how this organism may be dominating a particular environment.

In the case of B. dorei and type 1 diabetes in Finland, this has gone further, with the publication of three full genomes of this species from stool samples obtained in Turku.

This work led to the model described in our recent review [3].

The model was intended to serve as a platform to test the ideas presented, as well as to add new concepts.

That is, if B. dorei is associated with type 1 diabetes autoimmunity, what are the factors that contribute to its dominance in the gut microbiome of unhealthy children?


As stated in the review, a number of papers have shown that diet can affect Bacteroides populations in the human gut.

The authors propose the addition to fibre to our model for the dietary control of Bacteroides gut populations [1].

This is based on their recent study in the NOD mouse [4], the idea being that fibre in the diet may be an important component leading to type 1 diabetes and that this may be conferred by an increase in the Bacteroides population in the NOD mouse.

It is not clear whether this work in mice is translatable to the human condition.

The authors propose that a fibre-containing berry cocktail given to Finnish children at 4 months of age may be the trigger that precedes the increase in B. dorei at about 7.5 months of age.

Solid food is introduced to Finnish babies at 4–6 months of age [5, 6], so it is hard to identify the particular dietary components that contribute to a high B. dorei population, but we agree that soluble fibre is one candidate.


As stated previously [2], it has been known for many years that Bacteroides likely plays an important role in the digestion of complex carbohydrates in the human gut [7].

Nevertheless, there are precious few papers on the role of dietary fibre on Bacteroides populations in the human gut.

A recent paper by Lamichhane et al [8] reported a strong positive correlation between soluble fibre intake and the level of Bacteroides in the human gut.

Still other papers point to gluten as a contributor to a high relative proportion of Bacteroides species [9], while others suggest that high protein and animal fat is an important contributor [10].

It would not be surprising that all of these dietary factors play a role.

However, it is clear that the source of B. dorei in Turku, Finland, will never be resolved by studies in the NOD mouse, and a careful epidemiological analysis of the source of this bacterium in that city is warranted.


Decentralized wastewater treatment has been proposed as a strategy to help reduce potable water consumption (e.g., Wilderer and Schreff, 2000) and as a cost-efficient alternative to more centralized treatment (e.g., Engin and Demir, 2006).


Decentralized treatment systems are defined as the collection, treatment, and distribution of water and wastewater near the point of use or generation (Crites, 1998).

These decentralized systems reduce collection transport distances and have lower flow volumes that enable the use of smaller diameter piping, shallow installation depths, and vacuum and pressurized sewers (Nelson, 2005), all of which potentially reduce energy and material use.

Complementing centralized water-related infrastructure with decentralized facilities has been described as a "soft path solution" (Gleick, 2003), partly because decentralized facilities allow water services and quality to be tailored to end-use needs.


Decentralized systems are also more amenable to grey water separation technologies (Lienert and Larsen, 2006), which increase the potential reuse of wastewater and promote the return of treated wastewater within the watershed of origin (Massouda et al., 2009).

Decentralized wastewater systems can be located adjacent to areas with high demands for non-potable water, such as golf courses and public landscaping, thereby redirecting large volumes of water for reuse (Allen and Vonghia, 2005).


The significant capital investments required for centralized treatment infrastructure can be prohibitively high in developing countries (Rocky Mountain Institute, 2004) and decentralized wastewater systems are perceived as a more appropriate strategy with reduced costs, less maintenance, greater flexibility in planning for future growth, and reduced environmental impact (Engin and Demir, 2006).

For these reasons, institutions like the World Bank consider decentralized systems as an alternative to traditional centralized systems (Wilderer and Schreff, 2000).


While there are many potential benefits of decentralized systems, the inherent loss of scale economies relative to larger centralized systems may result in increased unit energy, cost, and materials associated with facility operation as treatment plant capacity decreases.

These benefits of centralized systems, however, are thought to diminish when building and maintaining the distribution and collection systems are considered (Lens et al., 2001).

A proper environmental analysis and comparison of centralized and decentralized wastewater systems requires expanding the evaluation scope beyond facility operation to determine the impact at each stage of the process, including infrastructure construction and maintenance as well as material production and transport.

This paper provides a framework for analyzing decentralized wastewater systems and evaluates the energy and environmental impacts associated with two currently operating case systems - one centralized and one decentralized - while accounting for the resources consumed and the pollutants released throughout the life-cycle of the collection, treatment, and distribution processes, as well as accounting for water treatment avoided through water reuse strategies available with decentralized systems.


This article deploys the Wastewater-Energy Sustainability Tool (WWEST), discussed in Stokes and Horvath (2010), to conduct a system-wide life-cycle assessment (LCA) comparison of centralized and decentralized wastewater systems.


The decentralized wastewater case study is based on an operating treatment system in northern California.

This community-scale system (Figure 1) provides wastewater treatment for a 47-lot suburban subdivision of Stonehurst in Martinez.

The treatment system has operated since the 4 early 1990's and has been described as a successful and innovative decentralized wastewater treatment strategy for California (Crites, 1998).

The details of this wastewater treatment system have been outlined in previous publications (Crites, 1998; Tchobanoglous et al., 2003).

The system was designed to annually treat nearly 20 million liters of liquid wastewater and operates at capacity.

Each lot includes a 5.7 m 3 concrete septic tank.

Septic tanks are commonly used in rural areas and can be found near 25% of U.S. homes (USEPA, 2005).

Effluent from septic tanks is typically distributed to an adjacent drainfield for aerobic treatment, requiring a large amount of open space.

The footprint for the septic tanks in the decentralized wastewater case study is reduced through the use of a community wastewater collection system that transports the septic tank effluent for nearby treatment.

Each onsite septic tank is connected to a 5-cm (two- inch) diameter sewer main.

Thirty-two homes are located uphill of the sewer main and are connected through small diameter gravity-forced piping.

Each of the other 15 homes, located downhill, has a small (0.2 kW) septic tank effluent pump (STEP) to transport wastewater to the sewer main.


Approximately 5 km of sewer-main piping connects effluent from the homes to a single wet-pump station that uses two 1.5 kW pumps to transport the effluent to a community treatment plant.

The treatment plant consists of a recirculating sand filter, where the wastewater is first sent to a recirculating tank and then pumped through a 0.6 m gravel bed approximately five times before being sent across an open three channel ultra-violet (UV) supply sump system for disinfection.

An effluent pump station then transports the treated water to an 11.4 m 3 hilltop dosing tank where the water is distributed to a 10,000 m 2 community soil absorption field.

Treated water in the dosing tank is also available for irrigation use through a subsurface drip system for a small nearby park.

Septic tank cleanings are arranged by each individual 5 homeowner and assumed to occur every five years for this study.

The removed biosolds are assumed to be transported to landfill.


The decentralized wastewater system is compared to a previously published WWEST case study for a large centralized wastewater utility in California (Stokes and Horvath, 2010).

The centralized system serves approximately half a million residents over a 200 km 2 area and includes conventional liquid and sludge treatment process streams.

The treatment processes used in this centralized plant are outlined in Figure 2.

The liquid waste is treated with primary sedimentation, pure-oxygen activated sludge, secondary clarification, and disinfection.

Solids are anaerobically digested and dewatered before disposal.


Table 1 summarizes the major energy and equipment demands that are used in the WWEST analysis for both wastewater systems.

Information regarding the systems was obtained through publications and direct communication with utility employees.

Methane from the treatment process is assumed to be flared for safety reasons (Sahely et al., 2006).

While flaring will convert methane biogas to carbon dioxide, a conversion efficiency of 95% was assumed to account for any incomplete combustion and leaks that may occur (Monteith et al., 2005).

Results are provided for the centralized plant assuming flaring.

For comparison, Table 2 includes the estimated results if the methane was used to generate electricity.


These data assume that the methane produced when treating 3785 m 3 (one million gallons) per day with anaerobic digestion will produce the equivalent of 26 kW (USEPA, 2011).

The centralized plant would produce 14,500 MWh per year assuming all the methane captured (95%) is used for electricity production.

The U.S. Environmental Protection Agency reports that biogas is used to offset energy use at approximately 8% of wastewater plants where it is technically feasible and at less than half of the plants where this strategy would be considered economically feasible (i.e., 6 payback in less than 7 years).

These statistics indicate there is significant potential for future improvements in energy consumption and related emissions in the wastewater industry.


Figure 3 compares the life-cycle energy use, separated by life-cycle phase and treatment phase, for the decentralized treatment system evaluated in this study with the centralized plant case study from Stokes and Horvath (2010).

The results show that the decentralized system used about 37 GJ of primary energy for every million liters of wastewater treated, while the centralized system used about 6.8 GJ for the same functional unit.

The increased life-cycle energy associated with the decentralized system can be partially explained by the significantly greater direct operational electricity use for the decentralized system, as presented in Table 1.

These operational electricity values for both the centralized and decentralized systems are obtained from utility meters that represent the entire treatment plants.

No data were provided to effectively disaggregate by process.

After normalizing for differences in treated waste volumes, utility records indicate that the decentralized system requires seven times more electricity to operate than the centralized system.


Additionally, the construction phase contributes significantly to the life-cycle energy of the decentralized system while the centralized system is dominated solely by the operational phase.

Dixon et al. ( 2003) estimate life-cycle energy use for two small-scale wastewater treatment systems that are of equal magnitude to the life-cycle energy use of the centralized system presented in this study.

However, those two small-scale designs - a reed bed system and an 7 aerated filter treatment unit- require minimal pump power at the treatment stage and pumping for collection and discharge was beyond of the boundaries of that analysis.


While the decentralized system in this study is a fairly low-technology design, the pumps required for collection, discharge, and recirculation through the sand filter, as well as the UV lights, require significant electricity during operation relative to the amount of treated water.

An effluent pump station is required in the decentralized system to transport treated wastewater uphill to the holding tank, the effects of which can be seen in the discharge category of life-cycle energy distribution in Figure 3.

The centralized treatment plant is located adjacent and above the final discharge into the San Francisco Bay so no effluent pumping is required.

Table 2 presents life-cycle energy and emissions of greenhouse gas and criteria pollutants for both the centralized and decentralized wastewater treatment system separated by activity.


The influence of operational electricity demand on life-cycle impacts is highlighted by the Energy Consumption category in Table 2.

The relatively large contribution of Energy Consumption to the total life-cycle impacts for both treatment systems supports previous work by Gaterell and Lester (2000) that suggests the majority of the environmental burdens from wastewater treatment are associated with consumption of energy during the operational phase.

Table 2 shows that the life-cycle energy required for Material Production is comparable to Energy Consumption in the centralized plant, which is partly due to the high embodied energy of chemicals used in the centralized treatment.

Energy Consumption is responsible for most of the life-cycle energy needs in the decentralized system, with Material Production and Onsite Equipment/Vehicle Use also contributing significantly due to the relatively greater influence of the construction phase.


When comparing greenhouse gas emissions (expressed in units of carbon dioxide equivalent, CO 2 (e)), the difference between the two systems increases, primarily due to 8 direct methane losses from the decentralized septic tanks, represented in the Direct Emissions category of Table 2.

Even after adjusting for new findings that indicate septic tank emissions are below previously established IPCC estimates (Diaz-Valbuena et al. 2011), the methane released from the decentralized system is still significantly higher compared to the centralized plant, which is designed to effectively flare methane produced during treatment.

Furthermore, generating electricity from methane is becoming increasingly utilized in central treatment plants (Connelly 2011; USEPA, 2011).

Methane emissions from landfilled biosolids contribute only slightly to overall greenhouse-gas emissions.

Both treatment systems are assumed to have solid waste disposed at a nearby landfill with an 85% efficient gas recovery system.


Table 2 shows that the difference in criteria pollutant emissions between the two systems scales closely with the difference in operational electricity consumption shown in Table 1.

This scaling is partly because electricity use is a significant source of criteria pollutants and both treatment plants are assumed to use electricity generated from California's average primary energy mix.

The greater difference in particulate matter emissions between the two systems is due to increased influence of tailpipe emissions from off-road vehicle equipment needed for construction and maintenance of the decentralized system, as indicated in the Onsite Equipment/Vehicle Use category of Table 2.

Off-road vehicle use also increases NO x emissions associated with the decentralized plant, but a greater difference between the two systems is not observed here since the centralized plant's NO x emissions also increase due to Material Delivery needs.


While septic systems are the most common form of decentralization, new forms of decentralized treatment such as membrane bio-reactors are being developed.

High-technology decentralized systems may have different life-cycle impacts than the systems evaluated in this paper.

Additional decentralized systems should be evaluated as these emerging technologies become established and reliable data become available.


Multiple factors influence the choice of wastewater treatment strategies, and decentralized systems provide many benefits including lower initial investment costs, increased flexibility, the opportunity to reduce potable water needs through wastewater reuse, and the possibility of reduced ecological impacts outside the scope of this analysis.

LCA methods are inherently limited and previous evaluation has shown that life-cycle energy impacts do not necessarily align with other qualitative considerations such as public amenity, ecosystem conservation, or aesthetics (Brix, 1999).

The results presented in this study indicate that along with any social and environmental benefits from decentralized treatment comes the potential for an increase in life- cycle energy and air pollutants.

Decision makers should be aware of this possible increase and the life-cycle impacts of proposed decentralized designs should be evaluated to minimize environmental burdens of future wastewater treatment development.


In the article ‘Energy and air emission implications of a decentralized wastewater system’ published in Environmental Research Letters (2012 Environ.

Res.

Lett.

7 024007), Shehabi et al compared a decentralized and a centralized system on the basis of energy use, greenhouse gas emissions and air pollutants, and claimed that economies of scale lower the environmental impacts from a centralized system on a per-volume basis.

In this comment, we present literature and data from New York State, USA to argue that the authors’ comparison between a small decentralized system (0.015 MGD) and a large centralized system (66.5 MGD) is unconventional and inappropriate [1].

The recent article by Shehabi et al (2012) compared a decentralized and a centralized system on the basis of energy use, greenhouse gas emissions and air pollutants, and argued that economies of scale lower the environmental impacts from a centralized system on a per-volume basis.

The authors’ analysis builds on a similar effort by Muga and Mihelcic (2008) that used a combination of economic, environmental and societal factors to evaluate the effectiveness of various wastewater treatment systems [2].


We found the comparison between a small decentralized system (0.015 MGD serving 100 residents) and a large centralized system (66.5 MGD serving 500,000 residents) unconventional and inappropriate for at least a few reasons.

First, the centralized treatment plant chosen was too large not only in comparison to the decentralized plant, but also in comparison to other centralized plants.

Muga and Mihelcic (2008) reviewed 16,255 wastewater treatment plants in the US and observed that over 80% of them have a capacity less than 5 MGD [2].

In the state of New York, only 16 of the over 600 municipal treatment plants have capacities greater than 65 MGD.

While these 16 plants constitute 62% of the installed capacity and serve 64% of the residents in the state, a disproportionate majority of plants are much smaller in size.

87% of the plants are smaller than 5 MGD, 69% are smaller than 1 MGD and 28% are smaller than 0.1 MGD [1].

This trend may be evident in other states as well, in some cases more pronounced depending on proportion of rural/urban populations.


Second, it would take 4600 small decentralized systems, such as the one described in Shehabi et al (2012), to make up a 66.5 MGD centralized treatment plant—an idea no city would entertain in its planning formulation.

Similarly, a large centralized plant would be an inappropriate and highly expensive solution for a rural community.

Large treatment plants achieve economies of scale in operational parameters such as energy and emissions, and are able to leverage rapid changes in technology, which smaller decentralized systems are unable to do.

Indeed, size plays a role even within centralized treatment plants.

The effluent violation rates for some parameters at small plants (0.01 MGD) are 10 times that of plants that are sized 100 MGD or larger [3].

The energy use per volume is higher in centralized plants sized 0.1 MGD or smaller, as compared to those sized up to 1 MGD and 5 MGD [2].


Many communities are not large and dense enough to warrant large treatment plants.

A comparison between the two methods of treatment is only fair if similar-sized plants are considered.

The inclusion of alternative scenarios with a large plant for a rural area or many small plants for an urban area can greatly enhance the validity of the model presented by Shehabi et al (2012).

Based on the data from New York, there are 34 plants that have capacities of 0.015 MGD or smaller serving 102 residents on average, nearly the same as that in case of the decentralized system described in Shehabi et al. Such a comparison is more likely to yield meaningful results and policy-worthy conclusions.


While GHG emissions and particulate pollution are critical factors in evaluating the efficiency of wastewater treatment systems, very rarely do decisions about construction of new treatment plants get made on those factors.

The lack of federal and state funds and an ageing infrastructure have forced many small communities to look toward decentralized solutions for managing wastewater.

It is very likely that decentralized treatment systems will have a resurgence in small communities, this time in a planned and well-designed manner.

Therefore, it is imperative that a fair comparison between the centralized and decentralized approaches be conducted to allow decision-makers and stakeholders to make informed choices.


Complementing centralized water-related infrastructure with decentralized facilities is being considered in some communities and a life-cycle perspective is needed for informed decision making.

Our 2012 study presents a framework for analyzing the environmental effects of decentralized wastewater systems.


While the analysis framework could be applied to cases with a variety of sizes, we evaluated two currently operating systems in California, one decentralized and one centralized plant with a much larger capacity.

The disparate scales of the two plants represent an ‘off-the-grid’ suburban neighborhood-scale system compared with a similarly sized neighborhood connecting to an adjacent large centralized plant.

Deciding whether or not to connect expanding developments to nearby centralized plants is a realistic scenario for future growth, making the treatment plants evaluated in our study a realistic choice for comparison.


The comment submitted by Vedachalam and Riha (2013) argues that the case studies compared in our paper (Shehabi et al 2012) were ‘unconventional and inappropriate’ because of the disparate scales of the two wastewater treatment plants (WWTP).

We do not argue with the commenters’ assessment that most WWTPs are smaller than the centralized case study system in the paper.

They have clearly shown this to be true in New York and likely in other areas as well.

However, comparing a large centralized WWTP with a small decentralized WWTP is neither ‘unconventional’ nor ‘inappropriate’.

The two systems compete, but also complement each other, and life-cycle perspective is needed for informed decision making.


The suburban setting of our evaluated decentralized systems—and the decision whether or not to connect to nearby larger centralized WWTPs—is a realistic scenario for future growth.

Based on our recent discussions with other engineering and planning academics, designing decentralized treatment strategies to serve new developments on the edge, or ‘at the margins’, of an urban or suburban region served by a centralized WWTP, regardless of size, may be a realistic strategy for meeting any of several different goals, such as: providing a local, non-potable water source; preserving strained infrastructure within a city’s core; and allowing for use of natural treatment processes, which may be more energy efficient.


In contrast to the one example of an application of decentralized systems (rural settings) described in the comment, we expect that the example of comparing an ‘off-the-grid’ neighborhood-scale system to a connection to a large centralized plant will be increasingly common.


Vedachalam and Riha (2013) conclude that we should have chosen a centralized plant of a similar size (approximately 0.015 MGD) to provide a fair comparison, essentially arguing that we should be attempting to compare two different configurations of similarly sized plants.

The paper they are suggesting has an entirely different goal.


As the abstract of our letter states, ‘this paper presents a framework for analyzing the environmental effects of decentralized wastewater systems and an evaluation of the environmental impacts associated with two currently operating systems in California, one centralized and one decentralized’.

In this case, the centralized plant has a larger capacity, but the analysis framework could be applied to cases with a variety of sizes.


The questions surrounding decentralized wastewater treatment contain many unresolved issues and more research topics remain to be evaluated.

Additional comparisons of decentralized WWTPs with smaller centralized plants would be interesting and valuable assessments.


We do not consider our letter to be the final word on the issue of decentralized treatment.

Instead, we intend our letter to initiate what we hope to be an extended discussion in the academic literature about the tradeoffs between centralized and decentralized systems.

We intend to do additional analyses in this area and welcome other perspectives and input on the issue.


Consider a set S of n points in three-dimensional Euclidean space, ~3, and a collection of t triangles with vertices in S. Clearly, if t is not too large it could be that any two triangles intersect only in common vertices or edges, if at all.

Indeed, this is possible for t almost as large as n 2.

Once t exceeds n 2, some of the triangles start having common interior points, no matter how the points of S are placed in space.

If t > ~n 2 we start to see crossin9 pairs, that is, pairs of vertex disjoint triangles with common interior points.

This basic fact can be used to show that for t > 2n 2 the number of crossing pairs is at least some positive constant times t4/n 6.

This extends a result of I-1] that for n points and t > 4n edges in R 2 there are at least some positive constant times t3/n 2 crossing edge pairs.


Why is this interesting?

A consequence of the lower bound for crossing triangle pairs is that n points in R 3 have at most some positive constant times n 8/3 k-sets, for any fixed k, 0 < k < n. A k-set of S is a subset S' = SnH, where H is a half-space and IS'l = k. For three-dimensional k-sets the first nontrivial bound of 282 T.K. Dey and H. Edelsbrunner O(n 3- x/343) was reported in [4].

This was improved to O(n a/3 log 5/3 n) and later to O(n 8/3 log 2/3 n) in [3] and [6].

This paper removes the remaining poly- logarithmic factor from the upper bound.

While the improvement is not dramatic, we feel the main contribution is the introduction of a new and more direct approach to proving bounds on k-sets.


A set V of three noncollinear points in R a defines a triangle crv = conv(V).

We say that two triangles have a nontrivial intersection if their intersection is neither empty nor a vertex or edge of both.

Two triangles, av and a v, can form three different types of nontrivial intersections, as illustrated in Fig. 2.1.

If crv c~ av r and U n V = ~, then we say that a v and av cross.


We also say that an edge crosses a triangle if it intersects the triangle without sharing a vertex with it.

A finite point set is in oeneral position if any k of the points, 1 < k < 4, are affinely independent.

A set S of n points in general position in ~3 defines a collection of (~) triangles, denoted (~).

For any we write x(S, T) for the number of crossing triangle pairs in T. Furthermore, x(n, t) = rain x(S, T).

The results of this section are summarized in the following theorem.


We begin with a trivial but most useful observation.


Notice that because of (2.2) the lower bound for x(n, t) implies the lower bound for y(n, t).

To see this, count a crossing between two triangles once for each edge crossing the other triangle.

Give one unit of credit to each such edge.

There are two credits per crossing and at most 3t edges.

Assume the lower bound for x(n, t) and use the pigeonhole principle to get an edge with more than c3(t3/n 6) credits, where c3 = 2cx/3.


Consider a plane that contains no point of S, and let I 0 and I t be two parallel lines in this plane so that It n conv(S) = ~. For each 0 < z < 1 define I, = zl o + (1 - z)ll.

As z increases from 0 to 1, l, translates from I t to lo.

The 288 T.K. Dey and H. Edelsbrunner number of halving triangles crossed by l~ changes only when l~ translates through an edge,


A simple rotation argument shows that the number increases or decreases by at most one.

Given lo we can choose 11 so that l~ translates through fewer than ~n 2 edges as follows.

Choose the plane through lo so that there are (n- 1)/2 and (n + 1)/2 points on the two sides.

There are (n 2 - 1)/4 edges ab that cross the plane and thus there is one side of l 0 with fewer than n2/8 edges.

It follows that lo crosses at most n2/8 halving triangles.

We prove an upper bound on the number of halving planes by selecting a line that crosses many triangles.

Suppose there are t halving triangles.

Condition (ii) of Theorem (2.1) implies that there is a line that crosses more than c3(t3/n 6) of them, with c 3 = r~. Because of (3.1), s 6) < n2/8 which implies the main result of this section.


H. The number of halving planes is closely related to the number of k-sets of S. Indeed, the number of k-sets, for k = (n - 1)/2, does not exceed three times the number of halving planes, see [5].

For other values of k consider the planes spanned by three points of S so that k - 1 points lie on one and n - k - 2 points lie on the other side.

Again, the number of such planes is proportional to the number of k-sets.

Furthermore, the same arguments imply the same upper bound as in Theorem (3.2) for any fixed k.


The bound in Theorem (3.2) slightly improves the ones in 1-3] and [6].

Besides this improvement, the main contribution of the new proof is the uncovering of the close relationship between k-sets and the combinatorics of crossings between edges and triangles in R 3.


To prove (ii) assume that no two triangles in T cross.

An edge that crosses a triangle a can only belong to triangles that share a vertex with a. There are at most three such triangles incident to that edge.

Remove this edge together with the at most three triangles.

Let k be the number of such edges that need to be removed until no nontrivial intersection remains.

We lose at most 3k triangles and decrease the total vertex degree to ~p, Es 61 - 2k or less.

It follows that pjeS p, eS is an upper bound on the number of crossing-free triangles.

The rest of the argument for the lower bound on x(n, r) is inductive and is inspired by the proof of a similar result for line segments in [1].

Specifically, we prove the following inequality.


The upper bound for y(n, t) in (ii) of Theorem (2.1) implies the upper bound for x(n, t) in (i).

To see this assume no edge crosses c4(t2/n 3) or more triangles.

Since there are at most 3t edges the total credit accumulated is less than 3c4(ta/n3).

Each crossing pair of triangles generates two credits which implies that there are fewer than c2(t3/n 3) such pairs, where c2 = 3c4/2.


To prove the upper bound on y(n, t) we choose the points of S on the moment curve M(z)= (z, T 2, z3) defined for all re R. For two points a = M(zl) and 286 T.K. Dey and H. Edelsbrunner b = M('c2) we write a ~( b if z 1 < z 2.

It is easy to establish that M intersects any plane in at most three points.


Let S be a set of n points in •3.

Assume that n is odd and that no four points are coplanar.

For three points a, b, c e S let a = abe be the corresponding triangle and let h be the plane that contains a, b, c. We call h a halving plane, and a a halving triangle, if there are (n - 3)/2 points on each side of h. The following extension of the two-dimensional Lovfisz lemma [7] has been used in [3] and [4].

We say a line crosses a triangle if it intersects the triangle but not any of its edges.


(3.1) A line crosses less than n2/8 halving triangles.


Maybe the most interesting extension of the methods in this paper is to four and higher dimensions.

It is possible to generalize all results of Section 2 to crossing pairs of (d - 1)-simplices in R d. Specifically, we can show that t > cn d- 1 (d - 1)- simplices defined by n points in R d form at least c'(d§ dtd-l~) crossing pairs.

However, to prove a nontrivial upper bound on the number of halving hyper- 289 planes, we need a nontrivial lower bound on the number of (d- 1)-tuples of (d - 1)-simplices that have a common point in their interiors.

While the main part of the induction in (2.4) generalizes to this situation, we have no good extensions of the base case in (2.3).


We should be more accurate.

Recent results obtained with methods from algebraic topology I-9] do imply a nontrivial base case, and induction can be used to get nontrivial bounds for (d - 1)-tuples of (d - 1)-simplices and for halving hyperplanes.

These bounds come out very similar to those derived in [2] and are therefore omitted.

An interesting question is whether the elementary methods of this paper suffice to derive such nontrivial bounds.


We consider the problem of bounding the complexity of the kth level in an arrangement of n curves or surfaces, a problem dual to, and an extension of, the well-known k-set problem.

Among other results, we prove a new bound, O(nk^{5/3}), on the complexity of the kth level in an arrangement of n planes in R3, or on the number of k-sets in a set of n points in three dimensions, and we show that the complexity of the kth level in an arrangement of n line segments in the plane is O(n\sqrt{k} \alpha(n/k)), and that the complexity of the kth level in an arrangement of n triangles in 3-space is O(n^2k^{5/6}\alpha(n/k)) [1].


The k-set problem is one of the most challenging open problems in combinatorial geometry.

The simplest variant of the problem is: Given a set S of n points in the plane in general position, and a parameter 0 ≤ k ≤ n - 2, what is the maximum possible number of lines that pass through a pair of points of S and have exactly k points of S in one of the open half-planes that they define [2].

In a dual setting, we are given a set L of n lines in the plane in general position, and want to bound the maximum possible number of vertices v of the arrangement A(L), so that exactly k lines pass below v. We denote this set of vertices by Vk.

The kth level of the arrangement is defined to be the closure of the set of points that lie on the lines and have exactly k lines below them.

The set of vertices of this level is Vk ∪ Vk-1 [3].


The k-set problem was first studied about 1970 by Erdős et al. [ 14] and Lovász [22].

These papers have established an upper bound O(n\sqrt{k}) and a lower bound Ω(n log k) on the desired quantity, for k > 0, leaving a fairly big gap.

The upper bound was slightly improved to O(n\sqrt{k/log* k}) by Pach et al. [ 25].

After the original submission of this paper, significant progress has been made by Dey [8], who improved the upper bound to O(nk^{1/3}), for k > 0.

Dey’s proof is based on some of the ideas presented in this paper [4].


In the dual setting, the problem can be generalized in an obvious manner: In the plane, we are given a collection 6 of n x-monotone curves, each being the graph of a continuous totally or partially defined function, and a parameter 0 ≤ k < n, and wish to bound the complexity (i.e., the number of vertices) of the kth level in the arrangement A(6), defined exactly as in the case of lines [5].

In this more general setting only two results are known: A recent seemingly weak, but elegant analysis by Tamaki and Tokuyama [28] yields the bound O(n^{23/12}) on the complexity of a level in an arrangement of n pseudoparabolas, which are graphs of total functions, each pair of which intersect at most twice [6].

We also mention the case of pseudolines, which in this context are graphs of continuous totally defined functions, each pair of which intersect exactly once, where a slightly larger lower bound of Ω(n log n) for the complexity of the median level is established in [21].

Our proof techniques and upper bounds apply equally well to the case of pseudolines.

Recently, Dey’s techniques have also been extended to the case of pseudolines [9], [29].


Similar extensions apply in higher dimensions.

In the primal setting, we are given a set S of n points in Rd in general position, and wish to bound the number of hyperplanes passing through d of the points such that one of the half-spaces bounded by the hyperplane contains exactly k points of S [7].

For d = 3, the best-known upper and lower bounds are, respectively, O(n^{8/3}) and Ω(n^2 log n) [4], [10].

For d > 3, the best-known upper bound is O(n^{d-cd}), for some exponentially small but positive constant cd [30].

Note that, in contrast to the planar case, these bounds depend only on n and not on k [8].

We can formulate the problem in an arbitrary dimension, in a dual setting: We consider an arrangement of hyperplanes, or, more generally, of surfaces that are graphs of continuous total or partial functions, and define the kth level of the arrangement exactly as in the planar case.

We now seek bounds on the maximum possible number of vertices (or of faces of all dimensions) of the level.

Except for the case of hyperplanes, which is equivalent to (a variant of) the k-set problem mentioned in the preceding paragraph, no nontrivial bounds for the entire range of values of k are known [9].


In spite of the sorry state of the problem, even after Dey’s improvements, we can obtain improved nontrivial bounds when k is small.

The probabilistic analysis of Clarkson and Shor [7] (see also [26]) yields fairly sharp bounds on the combined complexity of the first k levels in arrangements.

For the case of hyperplanes, for example, the bound is 2(n^{bd/2c}k^{dd/2e}).

For sufficiently small k, this gives a better upper bound on the complexity of a single level than the general bound stated above [10].


In this paper we make several contributions to these problems.

In the preliminary version of this paper [1], we briefly reviewed and simplified some old proofs of the upper bound O(n\sqrt{k}) for the original planar k-set problem (or, dually, for the case of the kth level in an arrangement of n straight lines in the plane).

These proofs are related to the proof technique of Gusfield [16], [17].

We also gave a simple proof of the dual version of what is known as the “Lovász lemma” that is used to prove the bound.

These techniques apply equally well to arrangements of pseudolines; see, for example, [15].

For the sake of brevity, this part is not included in this version of the paper [11].


We adapt our proof techniques to obtain the bound O(n^{3/2}) on the complexity of the kth level in an arrangement of n line segments (or “pseudosegments,” to be defined below).

The same bound also follows from a result by Katoh et al. [ 20].

We then describe two simple approaches that reduce the bound to O(n\sqrt{k}\alpha(n/k)), where \alpha(n) is the inverse Ackermann function [12].


We then proceed to study the k-set problem for higher-dimensional point sets (or, dually, though not quite equivalently, to bound the complexity of the kth level in a hyperplane arrangement).

In the preliminary version of this paper [1], we have observed that the O(n^{8/3}) bound in R3 can be immediately brought down to O(n^2k^{2/3}), if we exploit a simple improved version of the Lovász lemma in an arbitrary dimension derived in this paper.

However, using Clarkson and Shor’s technique, we improve this bound further to O(nk^{5/3}), in a manner that makes no use of the improved Lovász lemma [13].

Although the improved lemma has so far no significant applications, we include it here because we believe it to be of independent interest and an extension of it is needed for the case of triangles in R3.

Over the whole range of k > 0, the O(nk^{5/3}) bound is stronger than all previous bounds, including the aforementioned O(nk^2) bound on the overall complexity of the first k levels [14].

A similar improved bound, of the form O(n^{bd/2c}k^{dd/2e-cd}), can be obtained in any dimension d > 3, for the same constant cd > 0 obtained by Živaljević and Vrećica [30].

Again, this bound is the best-known upper bound, for all values of k > 0 [15].


Finally, we consider the problem of bounding the complexity of the kth level in an arrangement of n triangles in 3-space.

We first obtain a nontrivial bound of O(n^{17/6}) and then improve it to O(n^2k^{5/6}\alpha(n/k)).

Our bound strongly depends on an upper bound on the complexity of a single level in an arrangement of line segments in the plane [16].

As discussed above, Dey’s results supersede some of the bounds obtained in this paper, but they do rely on some of the machinery developed here.

Some other, more recent bounds derived here can be further improved by exploiting Dey’s results.

We have written this revised version of our paper in a manner that is mostly independent of Dey’s progress.

Nevertheless, for completeness, we conclude the paper with a brief overview of the connections between Dey’s results and ours [17].


We prove an O.n.k C 1/1=3/ upper bound for planar k-sets.

This is the first considerable improvement on this bound after its early solution approximately 27 years ago.

Our proof technique also applies to improve the current bounds on the combinatorial complexities of k-levels in the arrangement of line segments, k convex polygons in the union of n lines, parametric minimum spanning trees, and parametric matroids in general.


The problem of determining the optimum asymptotic bound on the number of k-sets is one of the most tantalizing open problems in combinatorial geometry.

Due to its importance in analyzing geometric algorithms [8], [9], [19], the problem has also caught the attention of computational geometers [5], [14], [18], [27], [29].

Given a set P of n points in <d , a k-set is a subset P 0 (cid:181) P such that P 0 D P \ H for a half-space H , and jP 0j D k where 0 • k • n. A close to optimal solution for the problem remains elusive even in <2.

In spite of several attempts, no considerable improvement could be made from its early bound of O.n.k C1/1=2/ given by [20] and [24].

Several proofs exist for this well-known upper bound [3], [5], [17], [29] which is quite distant from the best-known lower bound of ˜.n log.k C 1// [17].


Pach et al. made the first dent in this upper bound improving it to O.n.k C 1/1=2=log⁄.k C 1// [25].

Even such a small improvement in <2 was a distinguished result [25].

Recently Agarwal et al. [ 3] attacked the problem in the dual setting with a fresh look.

Although they could not improve the worst-case upper bound, several new approaches were put forward to estimate the complexity of k-levels in the arrangement of n lines.

By a well-known duality these results also apply to k-sets.

One of the approaches of [3], based on “convex chains,” inspired our proof for the new upper bound of O.n.k C 1/1=3/ in <2.


Our proof technique is surprisingly simple.

It uses the concept of crossings in geometric graphs [1] which was first used by us to prove an O.n8=3/ bound on three-dimensional k-sets [14].

Crossings in geometric graphs have been successfully used for many problems in combinatorial geometry.

See, for example, [12], [15], [16], [28].

It is expected that our approach would open up new avenues to solve the d-dimensional k-set problem, which remains largely unsolved for d > 3.

The only nontrivial bound known for d > 3 is insignificantly better than the trivial bound [4], [9], [30].

In spite of the miserable state of the problem, an exact asymptotic bound is known for the number of i-sets summed over all i • k. Alon and Gy˝ori [2] showed that this number is 2.nk/ in <2.

Clarkson and Shor [11] generalized the bound to 2.nbd=2ckdd=2e/ for <d .


Our proof technique also applies to establish a new O.nk1=3 C n2=3k2=3/ complexity bound for k convex polygons whose edges are nonoverlapping and lie in the union of n lines.

A number of other results follow from this bound.

An optimal 2.nk1=3/ bound on the complexity of n-element parametric matroids with rank k follows due to a result by Eppstein [21].

As an immediate consequence, we obtain an O.E V 1=3/ bound on the number of parametric minimum spanning trees of a graph with E edges and V vertices whose edge weights vary linearly with time.

A new O.n4=3/ bound for k-levels in arrangement with n line segments can also be derived from the aforesaid bound.


The paper is organized as follows.

We develop major tools for our proof in Section 2.

A new bound for k-sets in <2 is proved in Section 3.

Section 4 describes the application of our proof technique to other related problems.

Finally, we conclude in Section 5.


Chronic kidney disease is becoming a major public health problem worldwide [1].

While the number of patients requiring kidney replacement therapy in the USA was 340,000 in 1999, it increased to 651,000 in 2010 [2].

Chronic kidney disease, which progresses cres- cively and irreversibly, is associated with high cardiovas- cular risk and has a prevalence of 10-13% in the general population [3].

Glomerulosclerosis and kidney interstitial fibrosis, which cause the progression of chronic kidney disease, are targeted during treatment.

Chronic tubulointerstitial disease, which leads to chronic kidney disease, causes glomerular, vascular, and tubulointerstitial damage, eventually leads to interstitial scarring, fibrosis, and tubular atrophy.


It is critical to understand the link between the 'supersonic shear imaging' (SSI) technique, a novel and promising approach for assessing the elasticity of numerous organs, including the liver, and pathologically identifi- able interstitial fibrosis and overall histological score in chronic kidney disease.

Histopathological diagnosis with kidney biopsy gives important information for the prognosis and management of chronic kidney disease, while kidney biopsy has complications such as bleed- ing, fistula, and rarely death [4,5].

Proteinuria and eGFR (estimated glomerular filtration rate) are important and noninvasive biomarkers of chronic kidney disease, how- ever, studies for developing early and more sensitive biomarkers are ongoing [6].


In addition to biomarkers, observation of the course of the disease with advanced ultrasound or magnetic resonance-based or molecular imaging techniques is also promising [7].

In elastogra- phy, which is a novel ultrasound-based method, tissue RENAL FAILURE 2022, VOL. 44, NO. 1, 314-319 https://doi.org/10.1080/0886022X.2022.2035763 stiffness or elasticity is measured using ultrasound [8].

Tissue stiffness is measured using Young's modulus and expressed in kilopascals or pascals [9].

Young's elastic modulus, as evaluated by noninvasive elastography, correlates with the degree of interstitial fibrosis [10].

Among various elastography methods, the SSI method is the most appropriate method to evaluate the stiff- ness of natural kidneys [8].

This technique is a simultan- eous and quantitative method that has been successfully applied to curved surfaces for mapping tis- sue elasticity [9].


In our study, we investigated the relationship between the interstitial fibrosis detected in biopsy and the total histological score obtained by the scoring of the histopathological findings and the kidney tissue stiffness determined by the SSI method.


Fifty-seven native kidney patients with proteinuria who were hospitalized or examined at the outpatient clinic of a tertiary care university hospital with a kidney biopsy indication were included in the study.

The inclu- sion criteria were: patients who were over 18 years of age, in whom kidney biopsy was indicated, and those who showed cooperation to have elastography examin- ation.

Patients who did not give informed consent to participate in the study, underwent kidney transplantation, had severe heart failure and urinary obstruction, were on dialysis, had suffered from neuro- logical illnesses such as delirium dementia, and patients with data loss were excluded.


The patients' demographic information, diagnoses, kidney biopsy indications, laboratory results (urea, cre- atinine, glomerular filtration rate [GFR], proteinuria in 24-h urine sample), and pathology information were obtained from the patient files.

Ethical approval for the study was obtained from the Ethics Committee of Cerrahpaşa Faculty of Medicine at Istanbul University (No: 12452).


Supersonic shear imaging elastography was per- formed using a sonoelastography device (Aixplorer; SuperSonic Imagine, Aix-en-Provence, France) and 4 MHz transducers before biopsy in all patients.

The patients were placed on a stretcher in the supine pos- ition.

The left native kidney was visualized using B- mode ultrasound imaging.

The color scale formed as a result of the signals obtained from the inferior pole and the cortex of the kidney using SSI elastography was evaluated in kilopascals (kPa) using the Tsukuba scoring system.

The elastography measurements were made both in the sagittal and transverse sections.

Sagittal and transverse elastography images are shown in Biopsies were obtained from the same left kidney as the ultrasound measurement has taken place.

Biopsies of the patients were evaluated at the Department of Pathology.

The routine hematoxylin & eosin, Masson's trichrome, and PAS stainings were used to detect inter- stitial fibrosis.


The glomerular score, tubulointerstitial score, and vascular score were also calculated to evalu- ate the presence of interstitial fibrosis.

The kidney histo- logical total score was calculated by recording the glomerular score (3-12 points), tubulointerstitial score (3-9 points), and vascular score (3-6 points) according to the percentage of involvement, as suggested by Hu et al. [ 11].

Segmental and global glomerulosclerosis per- centages were evaluated with the glomerular score, tubular atrophy, tubular cell infiltration, and interstitial fibrosis percentages with the tubulointerstitial score, and the arterial hyalinosis percentage with the vascular score.

A total score of 9 was considered as a 'mild' impairment, a score of 10-18 as a 'moderate' impair- ment, and a score of 19 and above as a 'severe' impair- ment.

The correlation between the total histological score and elastography measurements was also eval- uated.


Apart from the total histological score, the patients were also evaluated based on the presence of interstitial fibrosis.

The patients were separated into two groups: those with interstitial fibrosis (n ¼ 6) and those without it (n ¼ 51).

Patients with interstitial fibro- sis in more than 25% of the pathological sample were assigned to the fibrosis group, whereas patients with interstitial fibrosis in 25% or lesser section of the sample or no fibrosis on pathological examination were assigned to the non-fibrosis group.

The presence of interstitial fibrosis in the biopsies was compared to the elastography measurements.


The SPSS for Windows v.17.0 software was used in all statistical analyses.

The data are expressed as mean ± SD.

The distribution of the variables and the relationship between them were evaluated with the independent samples t-test.

The intergroup elastogra- phy measurements, age, creatinine, and global sclerosis were analyzed with one-way analysis of variance (ANOVA).

The chi-square test was used for the analysis of the nominal data, while Pearson's correlation test was used to evaluate the correlation between the numerical variables.

The statistical significance level was set at p < .05.