Malabsorptive bariatric surgery in type 2 diabetic patients restores β-oxidation in pancreatic beta cells, doubles the glucose-NEFA sensitivity and reverts to normality the diabetic condition.

Since circulating NEFA levels (which together with glucose concentration determine the ISR) do not decrease significantly, it is likely that it is the normal- isation of insulin sensitivity that causes the reduction of insulin secretion.

A fivefold increase of insulin sensitivity, as was observed in our study population, is much higher than that usually obtained by insulin sensitising agents, and it is independent of weight changes.

Therefore, it is conceivable that the altered intestinal physiology produced by the surgical operation could be the causative factor of these findings.

In particular, the exclusion of part of the small bowel from food transit might act to avoid local production of factor(s) worsening insulin sensitivity.


We stress that the results of the present study cannot be extended to all type 2 diabetic patients since they derive from a specific category of patients where the insulin secretion is still high enough in spite of frank diabetes mellitus.


The authors declare that there is no duality of interest associated with this manuscript.


We describe here in a synthetic form the mathematical model of NEFA-glucose comodulation of beta cell insulin secretion [19].


As in other mathematical models of glucose-stimulated insulin secretion [20, 50], the ISR is given by the sum of a static and a dynamic component.

The static component is modelled here according to the view that a dual signalling pathway that involves both glucose and LC-CoA regulates the stimulation of insulin secretion [22] (see Fig. 5).

The kinetics of LC-CoA formation from exogenous NEFA is represented by a single rate constant k. Cytosolic LC-CoA has two possible fates: bind to CPT-1 and be transported into mitochondria to undergo β-oxidation, or be enrolled in diacylglycerol (DAG) synthesis and enhance the ISR, possibly via activation of protein kinase C. A key aspect of the model is that it describes in a very simplified way the inhibitory action of glucose (via malonyl-CoA) on the binding of LC-CoA to CPT-1 and thus on the rate of LC- CoA transport into mitochondria.

The following equation for the static ISR component S (expressed in nmol/min) has been obtained [19]:


where G and F are the glucose and NEFA concentration respectively, the coefficient p 1 is the beta cell sensitivity to both glucose and NEFA, p 2 is related to the rate of LC-CoA use in the cytosol, and p 3 expresses the efficiency of LC- CoA transport into mitochondria where LC-CoAs are β-oxidised.

The term in square brackets in Eq. 1 is a sort of 'potentiation factor' that accounts for the action of fatty acids on insulin secretion and the influence of glucose on fatty acid metabolism.

By adding the dynamic component to the static component, we obtain the expression for the ISR:


Because the parameter p 1 was found to change with the time during the 24 h meal test [19], we have added to p 1 a circadian oscillation [20].

Thus the glucose/NEFA sensitiv- ity coefficient is expressed as p 1 +δ sin(2πt/T+ϕ), where p 1 is the average value over the 24 h, δ is the amplitude of the oscillation, T is the period equal to 24 h, and ϕ is the phase at the initial time (t=0 taken at 08:00 hours).

The ISR model has the following unknown parameters: k, p 1 , p 2 , p 3 , δ, ϕ, k d , to be estimated from the data.


In the hypothesis that the LC-CoA transported into the mitochondria is completely β-oxidised, the rate of β-oxidation B (expressed as mmol/min per cell) can be determined as the difference between the NEFA influx into the beta cell and the rate of LC-CoA use in the cytosol.

This leads to the expression:


where V is the volume of the beta cell.

It may be seen that B decreases with the increase of the glucose concen- tration G.


To the Editor: In a recent paper, Salinari and colleagues reported normalisation of glucose metabolism associated with improved pancreatic beta cell sensitivity to nutrients derived from a mixed meal, 1 week after malabsorptive bariatric surgery (bilio-pancreatic diversion), in morbidly obese patients with type 2 diabetes [1].

This was observed before the patients had lost a significant amount of weight.

On the basis of timed serum insulin, C-peptide, NEFA and plasma glucose measurements, they calculated the effects of NEFA and glucose on the insulin secretion rate (ISR).

The plasma glucose level was decreased to within the normal range after the surgery, but the serum NEFA level was unchanged.

Using a mathematical model, the authors concluded that the improved ISR after bariatric surgery was due to the restoration of normal NEFA oxidation in the beta cell, which enhanced ISR despite the significant decrease in the plasma glucose level.

This study is of great interest to us because we consider the malonyl-CoA pathway and the resultant NEFA augmentation of insulin exocytosis to be vital for glucose-stimulated insulin secretion (GSIS), especially for the replenishment of the readily releasable pool of insulin granules during second-phase secretion [2, 3].

Prior to the visual demonstration and molecular elucidation of intracellular granule trafficking, this process was known conceptually as ‘potentiation’ [4], ‘augmentation’ [5] or ‘amplification’ [6].


The paper makes no mention of factors other than glucose and NEFA that are also involved in meal-induced insulin secretion, such as incretins [7], parasympathetic input and amino acids, to name but a few.

Of note, an increase in the serum level of the incretin glucagon-like peptide 1 (GLP-1) is documented 1 week after bilio-pancreatic diversion, and this is thought to be responsible for long-term effective weight reduction through satiety [7].

GLP-1 acts on the beta cells to increase the level of cyclic AMP, leading to replenishment of the readily releasable pool of insulin granules during GSIS.

This is an ATP-sensitive K+ channel-independent insulinotropic action [8].

Interestingly, NEFA also augments GSIS via a mechanism that does not involve this channel [5].

Salinari et al. use a mathematical model of glucose–NEFA comodulation to estimate the ISR [1].

Therefore, given the lowered basal glucose, lowered glucose excursion after the meal, unchanged NEFA and increased ISR, they will naturally conclude that the effect of NEFA has been enhanced/amplified.

However, in our opinion, the increased beta cell potentiation [8], achieved through the GLP-1–cyclic AMP pathway after surgery, is expected in their patients, in line with previous findings in patients who underwent the same operation, at the same postoperative time-point [7].

A different conclusion might have been reached if the incretin effect had been taken into account.

The inclusion of the positive effect of the incretin pathway would have at least reduced, and at most cancelled out, the calculated effect of restored β-oxidation on ISR after surgery [1].


In conclusion, by not considering factors other than glucose and NEFA in meal-induced insulin secretion, Salinari et al. [ 1] may have overestimated the effect of restored NEFA oxidation on ISR after bariatric surgery.

The data itself are very important and have considerably expanded our understanding of the physiology of bariatric surgery.

However, a more comprehensive analysis of the ISR, one that at least takes account of the incretin effect, would be desirable.


To the Editor: In response to the letter of Komatsu and Aizawa [1] concerning our recent paper in Diabetologia [2], we wish to make the following points.

Because of space constraints we were not able to clarify the roles of the different parameters included in our model in detail.

Although, as noted by Komatsu and Aizawa, glucose and NEFA are the only inputs used to obtain the insulin secretion rate (ISR), the model response is also determined by the values of the parameters p1, p2 and p3.

These parameters are estimated by fitting the model to the C-peptide experimental data.

The equation for estimating ISR contains a function that includes both glucose and NEFA levels.

This function contains the parameters p2 and p3, which are related to beta cell fatty acid oxidation.

The parameter p1 acts as a multiplier of this function, and represents a further factor involved in the potentiation of the ISR.

Incretin action, in particular, is implicitly accounted for by the parameter p1 of the model.

The value of p1 in a given experimental setting is thus related to factors other than NEFA and glucose that determine the ISR.


We have found that the parameters p2 and p3 markedly change after bariatric surgery (BPD), becoming very close to control values (Table 1).

It therefore appears that defective β-oxidation is reversed after BPD.

However, we do not imply that this is the major factor responsible for the improvement in ISR.

In fact, after BPD, the NEFA level is unchanged whereas the glucose concentration is decreased, probably because of the increase in insulin sensitivity.

Parameter p1 is doubled after BPD, but does not reach the value found in controls.

We interpret this fact as evidence that an incretin-dependent increase in beta cell sensitivity to glucose–NEFA has occurred.

The incretin effect implicitly resides in the value of parameter p1.

Figure 3a of our paper [2] shows the effects of BPD on the model response.


In a previous article we applied this model to data from healthy volunteers during a hyperglycaemic clamp after either water or butter ingestion [3].

After the water load, the parameter p1 was half the average value observed using the 24 h multiple-meal test.

Butter alone was able to double parameter p1, possibly via the incretin effect.

In fact, in this paper we suggested that the potentiation of insulin secretion mediated by gastrointestinal hormones does not occur after water ingestion but can take place after a butter test meal [3].

We appreciate the suggestion made by Komatsu and Aizawa [1] regarding the effect of NEFA on the replenishment of the readily releasable pool of insulin granules during the second phase of insulin secretion.

We recently proposed a mathematical model of insulin granule trafficking in the beta cell [4] in which the dynamics of the various granule pools, including reserve, docked and primed, immediately releasable and fused granules, are represented.

Unfortunately, the paper by Aizawa and Komatsu [5] escaped our attention.

Extension of the model to include the effect of NEFA would be of great interest.


Successful infection control is essential in the treatment of chronic osteomyelitis and infected non-union.

Adequate sur- gical debridement remains key to achieving this goal (Fer- guson et al., 2017; Lew and Waldvogel, 2004).

Surgical debridement should be accompanied by systemic antibi- otic treatment.

Problems include low antibiotic concentra- tions due to inadequate perfusion of the bone and the sur- rounding soft tissues (van Vugt et al., 2016; Romano et al., 2014; Geurts et al., 2011) as well as the development of an- tibiotic resistance and the formation of biofilms (van Ges- tel et al., 2015; Lindfors et al., 2017).


High concentrations can be achieved with local treatment, e.g., with gentamicin- containing polymethylmethacrylate (PMMA) beads.

Cur- rently, the gold standard in treating chronic osteomyelitis is a two-stage procedure that involves the use of antibiotic- containing PMMA beads in the first procedural stage (Geurts et al., 2011; Lindfors et al., 2017; van Vugt et al., 2019).


However, the PMMA beads must be removed in a further operation (Ferguson et al., 2017; Geurts et al., 2011), and, after releasing the antibiotics, they can themselves act as a E. Steinhausen et al.: Bioactive glass vs. autologous bone graft foreign body, thereby creating a base for bacterial biofilms (Romano et al., 2014; Lindfors et al., 2017; Ferguson et al., 2014; Rahaman et al., 2014).


Bone reconstruction is necessary after successful infection control.

Autologous bone graft is the gold standard and has osteogenic, osteoinductive, and osteoconductive properties (Ferguson et al., 2017; Calori et al., 2011; De Long et al., 2007; Egol et al., 2015).

However, the volume that can be achieved is limited, and donor site morbidity is considerable (Pape et al., 2010).


Various bone substitutes have been developed in recent years to complement or even replace autologous bone graft- ing (Egol et al., 2015; Fillingham and Jacobs, 2016; Kurien et al., 2013).

An ideal bone substitute should be osteoconduc- tive, osteoinductive, biodegradable, and biocompatible (van Vugt et al., 2016; Calori et al., 2011; Fillingham and Ja- cobs, 2016).


Bone substitutes loaded with antibiotics have been developed for the treatment of bone infections.

They usually contain gentamicin (Romano et al., 2014; McNally et al., 2016; Fleiter et al., 2014; Lalidou et al., 2014), or al- ternatively tobramycin (Ferguson et al., 2014; McKee et al., 2010) or vancomycin (Luo et al., 2016), and make it possible to achieve high local antibiotic concentrations with low sys- temic levels without incurring the disadvantages of PMMA beads (Ferguson et al., 2017; Lindfors et al., 2017; Ferguson et al., 2014; McNally et al., 2016; Fleiter et al., 2014; McKee et al., 2010; Luo et al., 2016).


Clinical evidence in this area is still limited, however (Ferguson et al., 2017; Geurts et al., 2011).


Bioactive glass S53P4 is an inorganic bone substitute with antibacterial, osteoconductive, osteostimulative, and angio- genic properties (Cunha et al., 2018; Coraca-Huber et al., 2014).

The results of existing clinical studies of bioactive glass are promising (van Gestel et al., 2015; Lindfors et al., 2017; Ferrando et al., 2017; Lindfors et al., 2010; Auregan and Begue, 2015).


Here, we compare the outcome of bioactive glass S53P4 versus autologous bone grafts for filling defects in patients with chronic osteomyelitis and infected non-union.


Bioactive glass (BAG) (Bioglass S53P4; BonAlive ® Bio- materials Ltd., Turku, Finland) is an inorganic bone sub- stitute containing SiO 2 , Na 2 O, CaO, and P 2 O 5 .

Following implantation, sodium and basic ions are released, causing a rapid increase in pH and osmotic pressure.

A silicon dioxide- rich layer is formed on the surface.

Calcium and phosphate groups migrate to the surface to form a hydroxyl-apatite scaf- fold.

Osteoblasts migrate into this scaffold, proliferate, dif- ferentiate, and stimulate osteogenesis.

These surface reac- tions not only stimulate the formation of new bone, but also have an antibacterial effect (Lindfors et al., 2017; Coraca- Huber et al., 2014; Lindfors et al., 2010).


In addition, in vitro studies have shown an angiogenic effect with increased secretion of the vascular endothelial growth factor (VEGF) (Detsch et al., 2014).


We conducted a retrospective analysis of patients treated in our institution for chronic osteomyelitis and infected non- union who underwent filling of a bone defect with either autologous bone graft or BAG between July 2013 and Jan- uary 2017.


Inclusion criteria were age > 18 years, chronic os- teomyelitis and infected non-union (positive bacteriology and/or positive histology), filling of the bone defect with ei- ther autologous bone graft or BAG, and minimum follow-up of 12 months.

Exclusion criteria were aseptic non-unions and lack of follow-up (Fig. 1).


Descriptive epidemiologic data and details of the pre-, peri-, and post-operative courses were compiled from the digital patient files and processed anonymously.

Follow-up was analyzed up to a January 2018 cut-off.


The primary endpoint was successful control of infec- tion during follow-up (no clinical, laboratory or radiological signs of infection, closed soft tissues).

Secondary endpoints were bone healing and functional outcome focusing on mo- bilization with full weight bearing.

Any complications were recorded.


Bone healing was quantified by evaluating X-ray or CT imaging.

Healing was defined as the presence of complete cortical bridging in three out of four cortices or with no evi- dence of the fracture line.


Multi-resistant pathogens were defined as pathogens re- sistant to most antibiotics or antibiotic groups (methicillin- resistant Staphylococcus aureus, multi-resistant coagulase- negative Staphylococci, multidrug-resistant Gram-negative bacteria and extended spectrum ß-lactamase bacteria).


All patients were treated exclusively by the specialized team of the septic surgery department.

Decisions regarding treat- ment method were taken individually for each patient based on surgeon preference.


In all cases, radical debridement was carried out prior to filling the defect.

The surgeons took care to completely fill the defect and to press the implanted BAG into the surround- ing bone.

New samples were taken intraoperatively from all patients.


All patients received antibiotic prophylaxis periopera- tively for previously detected pathogens.

If an unknown pathogen was present, the patients received cefazoline as the standard antibiotic used in our institution.

Patients with an uncomplicated course and sterile samples did not re- ceive postoperative antibiotic treatment.

Patients with a pro- J. Bone Joint Infect., 6, 73-83, 2021 https://doi.org/10.5194/jbji-6-73-2021 longed course with multiple previous revisions or with per- sistent detection of pathogens received postoperative antibi- otic treatment.

Antibiotic treatment was chosen according to the pathogen and the antibiotic resistance pattern.

Antibiotics were given intravenously for 3 weeks and orally for an addi- tional 3 weeks.


Treatment groups were compared using Fisher's exact test for binary data and the Mann-Whitney U test for continuous data.

A p value < 0.05 was considered statistically signifi- cant.

Given the sample size of about 80 cases, the power of this study is limited, especially for even counts.

Significant differences for a theoretical prevalence rate of 20 % could be expected only if the prevalence was < 5 % or > 40 % in the other group.

For continuous measures, the detectable differ- ence was about half a standard deviation.

Statistical analysis was performed using SPSS (Version 24, IBM Inc., Armonk, NY, USA).

Multivariate analyses were conducted for com- plications, recurrence of infection, and osseous fusion.


Fifty-one patients received bioactive glass S53P4 (BAG), 32 patients autologous bone graft (AB), and 12 patients a com- bination of bioactive glass and autologous bone graft.

The outcomes for these 12 patients are described separately at the end of this section but are not analyzed statistically.

There- fore, the following results represent a total of 83 patients (Fig. 1).


The autologous bone graft and the bioactive glass group were comparable with respect to sex, localization and kind of fracture, and the number of previous operations and flaps (Table 1).

However, the patients of the BAG group were significantly older and had undergone an unsuccess- ful prior attempt at defect filling significantly more often than those in the autologous bone graft group.

Staphylococ- cus aureus and coagulase-negative Staphylococci were the most common pathogens involved in both groups.

Gram- positive pathogens were found significantly more often in the bioactive glass group.

Gram-negative pathogens were de- tected in both groups without significant difference.

Multiple pathogens and multidrug-resistant pathogens also did not dif- fer significantly between the groups (Tables 1, 2).


In the BAG group, an average of 11 cm 3 bioactive glass was implanted (minimum 5 cm 3 ; maximum 30 cm 3 ).

Intraopera- tive samples showed persistent bacteria in 17 patients of the BAG group and in 11 patients of the AB group, indicating no significant difference between the groups.

However, pa- tients of the autologous bone graft group had undergone a change in pathogens significantly more often (BAG 3.9 %; AB 28.1 %; p = 0.001).

Forty patients of the BAG group and 29 patients of the AB group received postoperative antibiotic treatment owing to a prolonged course or persistent detection of pathogens.

No significant differences were found between the two groups regarding postoperative antibiotic treatment (Table 3).


Patients of the autologous bone graft group had significantly longer follow-up than patients of the bioactive glass group (p < 0.001).


Overall, major and minor complications were found in 22 patients of the BAG group and in 20 patients of the AB group, indicating no significant difference between the groups.

Re- currence of infection occurred in 15 patients of the BAG group and in 6 patients of the AB group.

Statistical analy- sis also failed to show a significant difference between the groups with regard to recurrence of infection.

Two patients (6.3 %) suffered complications requiring revision surgery af- ter removal of autologous bone from the iliac crest.

No com- plications associated with bioactive glass were observed.


Forty-seven patients of the BAG group and 31 patients of the AB group achieved full weight bearing during follow- up, indicating no significant difference in this respect.

How- ever, patients of the BAG group achieved full weight bear- ing significantly more rapidly (BAG 5.9 ± 4.1 months, me- dian 6 months; AB 10.7 ± 8.6 months, median 10 months; p = 0.018).


The results for bone healing were similar in both groups, with bone healing being seen in 39 patients of the BAG group and in 25 patients of the AB group by the end of follow- up.

Again, patients in the BAG group accomplished bone healing more rapidly (BAG 9.5 ± 7.0 months, AB 10.8 ± 9.0 months).

Eleven patients of the AB group underwent another procedure to fill the defect, significantly more frequently than in the BAG group (BAG n = 8; p = 0.049).

In addi- tion, patients of the autologous bone graft group underwent reoperations significantly more frequently (BAG n = 24; AB n = 24; p = 0.014).

The rate of below-knee amputation due to recurrent infection during follow-up was comparable be- tween the two groups (BAG n = 3; AB n = 2; p = 1.00) (Ta- ble 3).


Detection of multi-resistant pathogens was associated with a significantly higher rate of incomplete osseous fusion (p = 0.033) and was also associated with a 3-fold increase in the risk of complications (odds ratio 3.091, 95 %-confidence in- terval 1.128-8.470).

Male patients had a significantly greater risk of recurrent infection.

Finally, there was a 3.5-fold in- creased risk of recurrent infection after local or free tissue transfer (odds ratio 3.508, 95 %-confidence interval 1.155- 10.660).


Twelve patients had a defect filled with a combination of BAG and autologous bone graft.

Intraoperative samples re- vealed persistent bacteria in two patients (16.7 %).

Full weight bearing was reached after 4.8 months, on average.

Nine patients (75 %) achieved complete bone healing dur- ing follow-up after an average of 7.6 months.

Four patients (33.3 %) developed complications.

Only one patient (8.3 %) had recurrent infection during follow-up.


The goals of osteomyelitis therapy are successful control of infection and the reconstruction of the bone.

Bioactive glass S53P4 is an inorganic bone substitute with antibacterial, osteoconductive, and osteostimulative properties (Coraca- Huber et al., 2014; Heikkila et al., 1995).


Our comparison of the outcomes of patients with chronic osteomyelitis and infected non-union who had a defect filled with either autologous bone graft or BAG revealed no sig- nificant differences between the groups with respect to re- currence of infection, bone healing, full weight bearing, or complications in general.


Some studies have investigated the use of BAG in patients with chronic osteomyelitis.

However, the number of cases in these studies was usually small, and most had no control group (Romano et al., 2014; Lindfors et al., 2017, 2010; Au- regan and Begue, 2015; McAndrew et al., 2013; Drago et al., 2013; Malat et al., 2018; Oosthuysen et al., 2020).

Ex- isting comparative studies compare BAG either with other bone substitutes (Table 4) or with PMMA beads (the gold standard for a two-stage procedure) and with the objective of investigating successful control of infection and dead space management of bone cavities, but not bone healing.


To our knowledge, ours is the first study to compare BAG with au- https://doi.org/10.5194/jbji-6-73-2021 J. Bone Joint Infect., 6, 73-83, 2021 tologous bone graft in patients with an infected non-union that includes fracture healing as a secondary endpoint.


The reinfection rate of patients with chronic osteomyelitis is reportedly as high as 21.2 % (Walenkamp et al., 1998), and the overall reinfection rate after implantation of BAG is 0 %- 14 % according to the current literature (Romano et al., 2014; van Gestel et al., 2015; Lindfors et al., 2017, 2010; McAn- drew et al., 2013; Drago et al., 2013; Malat et al., 2018; Tan- war and Ferreira, 2020).

We saw higher reinfections rates, especially in the BAG group.

However, the number of pa- tients treated with BAG in published studies is usually small.

Moreover, the definition of reinfection or persistence of in- fection used by other authors is less strict than ours.

Romano et al. ( 2014) defined a "fair" outcome as a wound with pro- longed drainage or serum leakage of up to 6 weeks and a "poor" outcome as no wound healing for more than 6 weeks or one requiring surgical intervention.


Lindfors et al. ( 2017) defined the outcomes of their patients in a similar way.

These findings would be interpreted as persistent infection in our assessment.

These differences in definition may explain the increased infection rate in our BAG group.


On the other hand, the infection rate of our autologous bone graft group cor- relates with that reported in other studies.

Staphylococcus aureus and coagulase-negative Staphylococci were the most common pathogens involved in both groups.

The presence of multi-resistant pathogens was associated with an increased risk of complications regardless of treatment.

These results agree with those in other publications.

An additional risk factor for recurrent infection in both groups was a previous local or free tissue transfer, as also reported by Lindfors et al. ( 2017).


All factors considered, we could not establish an advan- tage of BAG compared to AB with regard to infection con- trol.

There were no significant differences between our two groups in other respects; that is, we found no clear disadvan- tage for BAG.


Our rate of complete osseous fusion is 77.1 % without signif- icant difference between the groups at latest follow-up.


Pub- lished studies do not report comparable rates (Romano et al., 2014; van Gestel et al., 2015; Lindfors et al., 2017; Ferrando et al., 2017; Lindfors et al., 2010; McAndrew et al., 2013; Drago et al., 2013).


This is unsurprising because these stud- ies investigated the use of BAG only as a bone void filler in chronic osteomyelitis.


However, they describe progressive incorporation of BAG, and radiographs therein show partial incorporation at latest follow-up, although the biomaterial re- mained visible (Romano et al., 2014).


In addition, they de- scribe the formation of a thickened cortex (van Gestel et al., 2015).


BAG was not fully incorporated in our patients, ei- ther, and we observed the phenomenon of a thickened cortex (Fig. 2).


Ninety-four percent of our patients reached full weight bearing.

Thus, we assume that, although osseous fusion re- mained incomplete on some radiographs, bone consolidation was adequate to ensure stability.

Basically, these patients re- gained a working extremity.


We observed high complication rates in both groups.

The ac- tual rate of reinfection was higher in the BAG group, but patients of the AB group needed significantly more reoper- ations and significantly more additional defect fillings.


Our rate of complications from harvesting autologous bone grafts from the iliac crest is comparable with that reported in other studies (Dimitriou et al., 2011).

However, only complications requiring revision surgery are mentioned in our study.

Pain after removal of autologous bone graft from the iliac crest and the resulting delay in mobilization are not recorded, al- though nearly all patients suffer these complications.

Last but not least, harvesting an autologous bone graft from the ante- rior or posterior iliac crest requires prolonged surgery.


These drawbacks do not apply to the use of bone substitutes.

In our study, bioactive glass was not associated with adverse effects.

This matches the experience in the other published studies.

So far, there has been no report of BAG-associated compli- cations or formation of bacterial resistance (Romano et al., 2014; van Gestel et al., 2015; Drago et al., 2013).

The toler- ability of BAG is even described as superior when compared to other bone substitutes (Ferguson et al., 2014; McNally et al., 2016).

Van Gestel et al. ( 2015) referred to bioactive glass S53P4 as a potential new gold standard.


Lindfors et al. ( 2017) analyzed the largest series of patients (n = 116) with chronic osteomyelitis to date, but without a control group.

BAG was used in a one-stage procedure in most of the cases, with excellent results.

The authors high- light the possibility of one-stage use due to the antibacte- rial effect as the key advantage of BAG.

Other authors agree and underscore its angiogenetic and antibacterial effects (van Gestel et al., 2015; McAndrew et al., 2013; Drago et al., 2013), although radical debridement remains indispensable (Lew and Waldvogel, 2004; Lindfors et al., 2017; Simpson et al., 2001).

Geurts et al. ( 2011) compared various antibiotic-loaded bone graft substitutes in active or suspected infection with PMMA beads.

The authors argue that PMMA beads remain the gold standard even if they require a two-stage procedure.


Hence, new biomaterials show great potential, but the level of available evidence is still limited.

In contrast to autologous bone graft, BAG has antibacterial and angiogenetic effects.

These effects are especially advan- tageous in often poorly vascularized bone.

Previous studies show that bacterial biofilms can cause the infecting organism to be resistant to systemic antibiotic concentrations of up to 1000 times greater than normal therapeutic levels (McKee et al., 2010).

Such concentrations cannot be achieved with sys- temic application.


Recently, Ferrando et al. ( 2017) compared bioactive glass S53P4 (n = 12) with calcium sulfate antibiotic beads (n = 13).

They did not find significant differences with regard to complication rates and recurrence of infection, but the num- ber of patients was small.

The authors conclude that BAG without adjunctive use of local antibiotics is as effective as calcium sulfate antibiotic beads.


Van Vugt et al. ( 2016) evaluated the use of various bone graft substitutes (Osteoset T, BAG, PerOssal, Herafill-G) in a systematic review (n = 15 studies).

In general, they criti- cize weak study design, small number of patients, low levels of evidence (LoE 2b-3b), and a high risk of bias.

Interest- ingly, the reinfection rate (primary outcome) was higher in high-quality studies.

No significant differences were found between the different bone graft substitutes.

The authors con- clude that the results are inconclusive.


To our knowledge, no study to date compares or proves the superiority of BAG over autologous bone graft with respect to its antibacterial effect.


We combined bioactive glass S53P4 with autologous bone graft in treating large bone defects (n = 12) (Fig. 3).

The cur- rent literature contains no comparable in vivo studies.

An in vitro study has analyzed and verified the antibacterial activ- ity of BAG as a bone graft extender in combination with AB (Bortolin et al., 2018).

However, to date there is no evidence to indicate which mixing ratio of bioactive glass and autolo- gous bone graft is optimal.

The optimal technique - in layers versus mixed - also remains to be determined.

Yet our results in a small number of cases are excellent with respect to our primary and secondary endpoints.

Therefore, we prefer the combination of BAG and AB for the treatment of large bone defects.

However, further evidence in favor of this approach is needed.


Our study has some limitations.

First, it is a retrospective observational study - although with a control group.

The groups are of different sizes, and patients in the BAG group are significantly older.

The level of evidence is weak.

The maximum follow-up differs between the groups, with signif-


To our knowledge, this is the first study to compare the use of BAG and AB in patients with chronic osteomyeli- tis and infected non-union.

The results are promising.

BAG seems to be an appropriate bone substitute, not only for fill- ing bone defects in patients with chronic osteomyelitis, but also for achieving fracture healing in cases of infected non- union.

In our study, BAG was neither superior nor inferior to autologous bone graft with regard to our primary and sec- ondary endpoints.

Prospective randomized studies - as rec- ommended by many authors - would be desirable but are not truly feasible.

An analysis with matched pairs may be an al- ternative.


I read with interest the recent paper by Steinhausen et al. ( 2021) [1].

This paper reports the outcome of a retrospective review of bioactive glass compared to autologous bone graft (ABG) in a staged surgical protocol for treating established bone infection.

The authors claim that bioglass is equally effective as ABG in eradicating infection but acknowledge that their evidence is weak.


The surgical treatment of chronic bone infection has been reported for well over a century.

In 1931, Jacob Kulowski published his classical series of 130 cases of osteomyelitis, treated by Orr’s method (Kulowski, 1931) [2].

His surgical debridement technique was similar to that described by Steinhausen et al. ( 2021) [1], and he achieved a recurrence rate of 24% (mean follow-up: 19 months), in the pre-antibiotic era, with no defect fillers.

In this new series of 83 patients, the recurrence rate with bioglass was 29% and 19% for ABG.

Clearly the addition of bioglass or bone graft has not improved outcome.


The authors state that the longer follow-up period for the ABG group is not a source of bias because “most complications occurred within 12 months”.

This same group previously published 50 of their 51 bioglass cases (Malat et al., 2018) [3] with a mean follow-up of 12.3 months and a recurrence rate of 14%.

This has increased to 29% with a longer follow-up (mean 20.5 months).

It would appear that the recurrence rate doubled in the 8 months after the first year.


There are no large randomized trials directly comparing the use of bioglass with other options, and the authors correctly report that outcomes of small studies are inconclusive.

However, there are two high-quality animal studies (Xie et al., 2009; Boot et al., 2020) [4][5].

In a rabbit model of MRSA osteomyelitis, Xie et al. ( 2009) [4] reported that bioglass was no more effective than debridement alone (success rate 36% for debridement and 18% for debridement and bioglass) but the addition of local antibiotics to calcium sulfate or bioglass increased success to 73% and 81% respectively.

Boot et al. ( 2020) [5] studied the effect of hydrogel and bioglass in an infected tibial nail model.

They showed that bioglass or hydrogel without antibiotics produced no reduction in infection, but vancomycin-loaded hydrogel could eradicate established infection.


It has been widely claimed that bioglass has the advantage of reducing the risk of antimicrobial resistance.

This paper shows that cases treated with bioglass required significant additional systemic antimicrobial therapy after repeated surgery or recurrence.

It has been shown that repeated surgery promotes change in bacterial cultures (Rupp et al., 2020) [6].

Recurrent, and often suboptimal, systemic therapy drives selection pressure and multi-drug resistance.

There is no evidence that local antimicrobial therapy contributes to this.

The recent study of Bidossi et al. ( 2020) [7] demonstrated that prolonged exposure to high-dose local antibiotics in a ceramic carrier was not associated with any adaptations in bacteria producing antimicrobial resistance.

In contrast to bioglass, the use of local antimicrobials may allow reduced systemic therapy and better antibiotic stewardship (Masrouha et al., 2018; Dudareva et al., 2019) [8][9].


Steinhausen et al. ( 2021) [1] also claim that “The tolerability of BAG is even described as superior when compared to other bone substitutes”.

We cannot find any evidence for this statement in their paper.

When comparing to the paper they quote as evidence (McNally et al., 2016) [10], the claim is not supported. If we compare recurrence rate, reoperation rate, failure of bone healing and amputation rate between the two papers, they report 29%, 47%, 23% and 6% respectively.

In our study of 100 cases, the figures were 4%, 3%, 13% and 0%.

We believe these outcomes would be more tolerable for patients.


Perhaps the most important conclusion from this paper is that serial debridement, followed by defect filling without local antimicrobials, is not a very effective treatment.

We would not recommend use of a hip or knee prosthesis with a 29% failure rate within 2 years.

This problem was identified in the 1970s and prompted the use of antibiotic-loaded PMMA beads by Klemm and Buchholz (Klemm, 1993) [11].

There is now encouraging evidence around the use of single-stage surgery, facilitated by modern local antibiotic carriers, with few complications and high infection eradication rates, even in the most complex cases (Masrouha et al., 2018; McNally et al., 2016; Ferguson et al., 2014; Lam et al., 2019; Pincher et al., 2019; Lorentzen et al., 2020; Drampalos et al., 2018; Jiang et al., 2020; Pesch et al., 2020; Hutting et al., 2021; Mifsud et al., 2020; Zhou et al., 2021) [8][10][12][13][14][15][16][17][18][19][20][21].


We read Martin McNally’s Letter-to-the-Editor [1] with great interest and are pleased to make precise the presentation of our results.

First, we will describe the differences between the patient collectives of the Zeitschrift für Orthopädie und Unfallchirurgie (ZOU) paper [2] and the current Journal of Bone and Joint Infection (JBJI) paper [3].

The inclusion criteria, the enrolment period and the follow-up period of the studies were different.

Fifty patients were treated in the ZOU paper.

Of these, 46 patients were treated with bioactive glass (BAG) and 4 patients with BAG in combination with autologous bone graft.

Ninety-five patients were treated in the JBJI paper.

Of these, 51 patients were treated with BAG, 12 patients with BAG in combination with autologous bone graft and 32 patients with autologous bone graft only.

Thirty of 50 patients (ZOU paper) were excluded for the JBJI paper because they did not meet the additional inclusion criterion “follow-up of at least 12 months” in the JBJI paper.

Therefore, 20 single individuals are published in both the JBJI and ZOU papers.

In these 20 patients, the follow-up data of 14 patients differ in the two publications.

The patients in the JBJI paper have a longer follow-up due to the 18-month longer follow-up period (until January 2018 instead of June 2016).

Six patients did not show up for further follow-up.

The first study (ZOU) is to be regarded as a study with preliminary results.

All patients in whom we implanted bioactive glass up to February 2016 were analyzed, even if they only had a follow-up of 2 months.

This is also listed as limitations in the ZOU paper.

We assumed the two studies to be independent of each other, with a new data set in the JBJI paper: preliminary results in the ZOU paper, longer follow-up in the JBJI paper, no control group in the ZOU paper.

Therefore, we presented the results of the ZOU study in Table 4 in the JBJI paper.

However, we agree that the ZOU paper should have been referenced in the methods section and in Table 4, given the connection between the two publications.


In order to be able to compare complication rates, the patient collectives must be comparable on the one hand, and the definitions must match on the other hand.

A well-known problem of the diagnosis “osteomyelitis” is the extreme heterogeneity.

Reinfection rates of 0 % up to 32.8 % are reported in the literature, depending on the definition and the treatment algorithm [4][5].

In the JBJI study, there is a higher number of patients with complex and therapy-refractory courses compared to the ZOU paper.

In particular, patients of the JBJI study had a longer – unsuccessful – course of disease with an average of 33.8 months from accident to defect filling with bioactive glass, while patients of the ZOU study had an average of 21.5 months.

There may be a negative bias due to our retrospective study design: patients without complications or reinfection may not have visited during our consultation hours any more.

Thus, patients without reinfection have more frequently a follow-up of < 12 months and therefore could not be included in the current JBJI study.

In addition, the definition of reinfection or persistence of infection used by other authors is less strict than ours.

Other authors [6][7] define a “fair” outcome as a wound with prolonged drainage or serum leakage of up to 6 weeks.

These findings would be interpreted as persistent infection/reinfection in our assessment.

The differences in definition as well as in the patient collective may explain the increased complication and reinfection rate in the JBJI study.

Overall, the comparability of our two studies is limited, just like the comparability of complication rates with other studies.


As Martin McNally and we state, there are no large randomized trials directly comparing the use of bioactive glass.

In our opinion, results of animal or in vitro studies – even if they are of high quality – are not sufficient to compare with the results of in vivo studies.

However, there are studies that prove the antibacterial effect of BAG without addition of local antibiotics.

Van Vugt et al. [ 8] evaluated the use of various bone graft substitutes in a systematic review.

No significant differences were found between the different bone graft substitutes (with and without adjunctive use of local antibiotics).

According to our standard at that time, we used the synergy of the local antimicrobial effect of bioactive glass and systemic antibiotics according to the antibiotic resistance pattern to achieve optimal efficacy.

A change in pathogens was seen in only 2 % of our BAG patients.

Independent of the effectiveness, some authors describe the tolerability of BAG as excellent.

To our knowledge, there have been no reports of bioactive glass-associated complications.

This corresponds to our experiences.

Therefore, we consider the tolerability of BAG to be excellent.

This statement in our discussion section does not refer to the patient’s outcome (recurrence rate, failure of bone healing, amputation rate).

In contrast to the excellent tolerability of BAG, our – unpublished – individual experiences with an absorbable gentamicin-loaded calcium sulfate/hydroxyapatite biocomposite were different.

Its use resulted in long-lasting drainage of white fluid like the liquefied bone substitute in most of our patients.

This phenomenon of increased drainage of white fluid is also described by other authors using this bone substitute [9][10].


Lastly, Martin McNally concludes that serial debridement followed by defect filling without local antibiotics is not an effective treatment.

We discussed the one-stage versus two-stage procedure in our JBJI paper.

In fact, several authors highlight the possibility of the one-stage use of BAG as well as of other bone graft substitutes.

However, our standard was a two-stage procedure during the inclusion period of the JBJI study (July 2013 to January 2017).

In addition, our influence of the increased number of previous operations is limited.

Most of our patients had already been treated in other hospitals before, and many of the previous operations were required due to large soft tissue defects.

Data availability.

The data sets analyzed during the current study are available from the corresponding author on reasonable request.


The Gene Expression Omnibus (GEO) repository was established in 2000 (1) to host and freely disseminate high-throughput gene expression data.

The database is built and maintained by the National Center for Biotechnology Information (NCBI), a division of the National Library of Medicine (NLM), located on the campus of the National Institutes of Health (NIH) in Bethesda, MD, USA.

The data are contributed by the research community, often in compliance with grant or journal directives that require data to be made publicly available, thus allowing others to access, evaluate and re-analyze results.


The three principal objectives of this project are to:


(i) provide a robust, versatile database in which to effi- ciently store high-throughput functional genomic data; (ii) offer the simplest submission procedures and for- mats that support complete and well-annotated data deposits from the research community; and (iii) provide user-friendly mechanisms that allow users to query, locate, review and download experiments of interest.


The fulfillment of these goals can be assessed in terms of database growth and usage.

Today the database holds over 10 000 experiments comprising 300 000 samples, 16 billion individual abundance measurements, for over 500 organisms, submitted by 5000 laboratories from around the world.

The database typically receives over 60 000 query hits and 10 000 bulk FTP downloads per day, and has been cited in over 5000 manuscripts.


In recent years, microarray technology has seen an explo- sion of applications that go far beyond analyzing gene expression levels.

Examples of such studies include those that examine genome single nucleotide polymorphism (SNP) and copy number variations (commonly called 'array comparative genomic hybridization', or aCGH), genome-protein binding surveys (commonly called 'chromatin immunoprecipitation on chip', or ChIP-chip), and various epigenomic factors such as nucleosomal posi- tioning and genome methylation status.

Additionally, non-array-based methodologies such as next-generation sequencing are increasingly being applied to such genome-wide investigations (2).


Despite the fact that GEO was initially set up to store gene expression data generated by microarrays and serial analysis of gene expression (SAGE), the flexible design of the database allows these non-expression or alternative high-through- put data types to be similarly hosted with little extra effort or overhead.

Thus, we have been accommodating to requests to accept such data and extended the minimal standards to fit these types.

In fact, at the time of writing, over 15% of the data in GEO are non-expression data.

Consequently, the name of the database, Gene Expression Omnibus, has become somewhat misleading and perhaps confusing to users.


To address this concern, the non- expression data have recently been placed under a new division called 'Omix', which denotes a mixture of 'omic data.

Other than a handful of minor issues specific to cer- tain data types, the submission and download procedures and formats for Omix data are largely the same as for GEO.

Additionally, equivalent levels of data reporting standards as established for expression data (3,4) are being applied to these other 'omic types.

This includes requiring raw data, processed data, protocols and ade- quate sample and experiment descriptive data.


All experi- ments in GEO and Omix have been newly assigned into broad experiment types, making it much easier for users to locate specific data or technology types.


GEO recently began processing high-throughput sequence data submissions (5-7).

It can be expected that next- generation sequencing technologies will become widely used and have a considerable impact on genome-wide sur- veys (2).

GEO accepts sequence data for studies that examine gene expression, gene regulation, epigenetics, or other studies where measuring sequence abundance is cen- tral to the experiment design.

GEO hosts the processed and analyzed sequence data, together with sample and experiment metadata; raw data files are brokered to NCBI's Short Read Archive sequence database, ensuring that these sequence data are integrated with NCBI's col- lection of sequence-specific resources (8).


As discussed in the Introduction section, the GEO data- base archives a wide variety of rapidly evolving, large- scale, functional genomic experiment types.

These experi- ments generate data of many different file types, formats and content which consequently present considerable challenges in terms of data handling and querying.

The GEO database has built-in flexibility to accommodate diverse data types.

Notably, tabular data are not fully granulated in the core database.

Rather, they are stored as plain text, tab-delimited tables that have no restrictions on the number of rows or columns allowed.


Some columns, however, reserve special meaning, and data from these are extracted to secondary databases and used in downstream query and analysis applications as described in the GEO DataSets section.

Accompanying supplementary and native file types are linked from each record and stored on an FTP server.

Contextual biological and other descriptive metadata are stored in designated fields within database tables with appropriate relations and restrictions.


The overall structure of the core GEO database remains as described previously (1,9).

Briefly, data submitted to GEO are stored in a relational MSSQL database partitioned into three entity types:


A Platform record is composed of a summary descrip- tion of the array and a data table defining the array tem- plate.

For sequence-based technologies, the Platform lists the sequences detected and identified in that experiment.

Each row in the table corresponds to a single feature, and includes sequence annotation and tracking information as provided by the submitter.

The table may contain any number of columns allowing thorough annotation of the array.

Each Platform record is assigned a unique and stable GEO accession number with prefix GPL.

A Platform may reference many Samples that have been submitted by multiple submitters.


A Sample record is composed of a description of the biological material and the experimental protocols to which it was subjected, and a data table containing abun- dance measurements for each feature on the correspond- ing Platform.

The table may contain any number of columns in which to comprehensively present results.

The metadata fields may hold very large volumes of text to allow elaborate descriptions of the biological source and protocols.

Each Sample record is assigned a unique and stable GEO accession number with prefix GSM.

A Sample must reference only one Platform and may be included in multiple Series.

A Sample record will typi- cally include supplementary files containing the raw (non-normalized) measured data, linked with the primary record.


A Series record defines a set of related Samples consid- ered to be part of a study, and describes the overall study aim and design.

Each Series record is assigned a unique and stable GEO accession number with prefix GSE.

Series records may contain one or more summary tables and supplementary files.


The submitter-supplied objects described above are very heterogeneous with regards to the style, content and level of detail with which the experiments are described. But despite this diversity, all expression-based submissions share a common core set of elements: (iii) text describing the biological source and experiment aim.


Through a procedure that employs both automated data extraction and manual curation, these three categories of information are captured from the submitter-supplied records and organized into an upper-level object called a GEO DataSet.

A DataSet represents a collection of con- sistently processed experimentally related Sample records, summarized and categorized according to experimental variables.

Each DataSet is assigned a unique GEO acces- sion number with prefix GDS.

DataSets are a means for transforming diverse styles of incoming data into a rela- tively standardized format upon which downstream data analysis and data display tools can be built.

At this time, only expression-based DataSets are being generated.


DataSets provide two distinct renderings of the data (Figure 1):


(i) an experiment-centered representation that encapsu- lates the entire study.

This information is presented as a DataSet record which comprises a synopsis of the experiment, a breakdown of the experimental vari- ables, access to auxiliary objects, several data display and analysis tools and download options; and (ii) a gene-centered representation that presents quantita- tive gene expression measurements for one gene across a DataSet.

This information is presented as a GEO Profile, and comprises gene identity annotation, DataSet title, links to auxiliary information and a chart depicting the expression level and rank of that gene across each Sample in the DataSet.

Gene Nucleic Acids Research, 2009, Vol. 37, Database issue D887 annotation is derived from querying sequence identi- fiers (e.g. GenBank accessions, clone IDs) with the latest Entrez Gene and UniGene databases, an impor- tant point given the dynamic nature of gene annota- tion (10).


Great emphasis has been placed on making data deposit procedures as simple as possible for submitters, while not compromising the level of experimental annotation required (11).

Four submission options are offered: web forms, spreadsheets, a plain text format and an XML format (Table 1).

All these formats are designed to capture all components of the MIAME checklist (4).

Deciding which method to use depends on the volume, type of data to be submitted and current data format.

To further ease the submission process, native files are requested where possible (e.g. Affymetrix CHP and CEL files).

No matter what deposit method is used, the final GEO records will look similar and contain equivalent information.

A skilled team of curators is on hand to assist researchers should any questions arise about submission procedures (email: geo@ncbi.nlm.nih.gov).


All data undergo syntactic validation upon upload.

A member of the curation team reviews each record to ensure that data are organized correctly and contain suffi- cient information to interpret the experiment.

If content or structural problems are identified, or if critical MIAME components are missing, the curator works with the sub- mitter until the issue is resolved or explained.

Submissions are typically approved within 2-5 days, but expedited approval can be performed on request.

Researchers are provided the capability to update their records at any time.

Records may be kept private until a manuscript describing the data is published.

Submitters may generate a temporary reviewer URL that grants anonymous, con- fidential access to their private data, typically via a journal editor.

Guidelines for reviewers and editors regarding how to access and evaluate private data are provided at http:// www.ncbi.nlm.nih.gov/projects/geo/info/reviewer.html.


Given the wide scope of biological projects and organisms represented in GEO experiments, it is crucial to provide effective query tools so users can quickly locate, analyze and visualize data relevant to their specific interests.

A summary of the main query features, and their location and purpose, is provided in Table 2.

Figure 2 depicts a schematic overview of the query workflow and how the various features and tools are interlinked.


NCBI's powerful Entrez (PubMed-like) search and link- ing system serve as the basis for most queries; Entrez GEO DataSets contains experiment-centered data and Entrez GEO Profiles contains gene-centered expression data.

Relevant material is located simply by typing in keywords or fielded Boolean (AND, OR and NOT) phrases.

Additionally, several auxiliary tools feed into Entrez, including the cluster heat maps and the 'Query group A versus B' tool.


A rich complement of Entrez links is generated to con- nect data to related information: inter-database links reci- procally connect GEO to other NCBI resources such as PubMed, GenBank and Gene; intra-database links con- nect genes related by expression pattern, chromosomal position or sequence.

Entrez search retrievals can be sorted and filtered by various flags and criteria, and down- loaded by various mechanisms.

Advanced Entrez features allow generation of multipart fielded queries, or can join multiple queries to identify overlapping results.


GEO provides several graphical renderings that greatly facilitate interpretation and visualization of expression data, including:


(i) interactive pre-calculated hierarchical and on-the-fly k-means/k-medians cluster heat map images that may hint at groups of coordinately regulated genes.


(ii) Expression profile charts that track the activity of one gene across all Samples in a DataSet.

A break- down of the experimental design is provided along the bottom of the chart, helping the user to quickly assess whether expression levels are shifting with experimental variables.


NCBI's GEO public archive stores massive volumes of published high-throughput functional genomic data gen- erated by the international research community.


In addition to archiving data, tools are provided to assist users of all levels of expertise to quickly search, query, analyze, visualize and download these data.

These features employ classic data reduction and filtering meth- ods, succinct displays designed for human scanning, and extensive linking with disparate but related data sources.


Looking at the literature, it is apparent that GEO is used routinely as a primary data resource by the research community.

Hundreds of third-party publications cite GEO data as evidence to support or complement indepen- dent studies, or use GEO data as the basis of statistical or analytical hypotheses or tools (http://www.ncbi.nlm. nih.gov/projects/geo/info/ucitations.html).


As high-throughput technologies advance, large-scale functional genomic datasets are becoming easier and cheaper to generate.

However, a major challenge remains in translating diverse sets of functional genomic data into context, i.e. integrating these datasets with each other and, ultimately, making correlations with observable pheno- types.

Collecting and archiving comprehensive 'omic data- sets in common formats at one public location like GEO is The GEO database and tools continue to undergo intensive development and modification aimed at enhan- cing the experiences of both data submitters and data consumers.

The submission pipeline and data transfer mechanisms will continue to be upgraded, and we plan to develop additional data retrieval and mining features, particularly for the novice user.


The Epigenomics database at the National Center for Biotechnology Information (NCBI) is a new resource that has been created to serve as a comprehensive public resource for whole-genome epigenetic data sets [1].

Epigenetics is the study of stable and heritable changes in gene expression that occur independently of the primary DNA sequence [2].

Epigenetic mechanisms include post-translational modifications of histones, DNA methylation, chromatin conformation and non-coding RNAs [3].

It has been observed that misregulation of epigenetic processes has been associated with human disease [4].

We have constructed the new resource by selecting the subset of epigenetics-specific data from general-purpose archives, such as the Gene Expression Omnibus, and Sequence Read Archives, and then subjecting them to further review, annotation and reorganization [5].

Raw data is processed and mapped to genomic coordinates to generate ‘tracks’ that are a visual representation of the data.

These data tracks can be viewed using popular genome browsers or downloaded for local analysis.

The Epigenomics resource also provides the user with a unique interface that allows for intuitive browsing and searching of data sets based on biological attributes.

Currently, there are 69 studies, 337 samples and over 1100 data tracks from five well-studied species that are viewable and downloadable in Epigenomics [6].


Interest in the field of epigenetics has exploded over the previous decade.

Epigenetics, strictly defined, refers to the study of stable and heritable changes in gene expression that are not mediated by the primary DNA sequence [1,2].

Epigenetic mechanisms participate in processes such as regulating gene expression, homologous recombination and DNA repair [3].

Individual epigenetic features are often called ‘marks’ because they are stably tied to specific genomic locations and may be propagated through many rounds of cell division, yet have the ability to be ‘erased’ later as cells undergo differentiation or are exposed to extra-cellular stimuli and environmental cues [4,5].

Several types of epigenetic marks have been identified and intensely studied.

These include post-translational modifications of histone proteins, DNA methylation, chromatin organization (DNase hypersensitivity) and non-coding regulatory RNA [3].

Just as ordinary mutations are known to contribute to disease, so too can corruption of normal epigenetic states (‘epimutations’) [7].

Indeed, many cancers, degenerative diseases and metabolic disorders have been shown to be the result of mis-targeting of DNA methylation or deficiencies in pathways for histone modification [8,9].

We may consider the collection of epigenetic marks across the genome to constitute the cellular ‘epigenome’.

The study of the epigenome, or epigenomics, refers to identifying what is literally ‘on’ the genome (the prefix epi- indicating above), and how these phenomena impact global gene expression, DNA mediated processes, and subsequently, development.

Studying epigenomes presents more of a challenge because each cell type may have a different configuration of features and, undoubtedly, additional epigenetic marks have yet to be discovered [10].


The development of chromatin immunoprecipitation (ChIP) as an experimental technique was a major breakthrough for the field of epigenetics [11,12].

ChIP allows for the genomic localization of chromatin associated proteins.

Typically, growing cells are treated with formaldehyde to induce protein–DNA crosslinking, followed by lysis and physical/enzymatic disruption of the chromatin.

Antibodies that specifically recognize epigenetic features are used to immunoprecipitate the protein–DNA complexes.

These antibodies can be specific to modified histones, histone modifying enzymes, transcription factors, or even modified nucleotides.

Following the immunoprecipitation, the DNA is isolated from the protein–DNA complexes for analysis.

If a particular epigenetic feature is localized to a specific genomic region, DNA representing that region will be enriched in the immunoprecipitate.

In conjunction with microarray analysis (or more recently, next-gen sequencing), these genomic regions can be identified [13].


It is common to represent epigenetic data as genome ‘tracks’.

The output of a next-gen sequencing experiment is millions of short DNA sequences, which are then aligned to a genome sequence.

Sequences that are enriched in the experimental material (an immunoprecipitate, say) will occur multiple times and form visually discernable peaks when represented graphically in a genome viewer.

A commonly used data structure for this type of track is the ‘wiggle’ format developed for use with the UCSC Genome Browser [14].

Because files in this format are usually given a .wig file extension, we will hereafter refer to them as ‘WIG files’.

Further advancement of these technologies has enabled genome-wide epigenetic analysis and as a result, massive amounts of data have been generated characterizing genomic localization of histone modifications, DNA methylation, smRNA and miRNA expression, and chromatin accessibility in numerous organisms and cell types [15].


With the renewed interest in epigenetics and the methodological advances in whole-genome analysis, in 2007, the NIH launched the Roadmap Epigenomics Project [16].

Among its aims are the development of reference epigenome maps from a variety of cell types and unraveling the relationships between the epigenomic landscape and human disease.

Complementary to this effort are the ENCODE (ENCyclopedia of DNA Elements) project and the corresponding modENCODE project for model organisms [17,18].

Although they are focused on identifying functional DNA elements in the genome, many of these sites may be epigenetically regulated or participate in epigenetic regulation.

In addition to these large projects, data from individual laboratory projects are incorporated on an ongoing basis.

The Epigenomics database is being created as public resource to provide access to these data.

It aims to provide both users familiar with the epigenetics field and novice users with a simple and intuitive interface to view, explore, analyze and manipulate these data.


Content in the Epigenomics resource is derived primarily from data originally submitted to archival databases at the NCBI, specifically, the Gene Expression Omnibus (GEO) and the Sequence Read Archive (SRA) [5,19].

Although originally established to warehouse gene expression data, GEO has become a general-purpose database for molecular abundance data from wide variety of experiment types, including those aimed at epigenomics.

In addition to the raw abundance data, extensive meta data may be provided with GEO submissions in order to fully describe the biological and experimental context.

In recent years, measurements of molecular abundance are increasingly being generated by the use of next-gen sequencing-based approaches.

SRA serves as a repository for raw next-gen sequence data, together with detailed information on the sequencing instrument and other experimental variables [20].


In constructing the new Epigenomics database, we have identified the subset of GEO and SRA data that pertain to epigenomics, subjected them to additional review, and reorganized them in a fashion that is more useful for epigenomics researchers.

In many cases, data producers provide WIG files as part of their GEO submissions, which allows them to be directly leveraged in the Epigenomics resource.

However, because some submissions either lack WIG files or have files based on older genome assemblies, we have developed a pipeline for generating epigenetic tracks that primarily uses the processed output from the Bowtie aligner [21].

The Epigenomics database currently has data tracks for epigenetic features, including histone modifications, DNA methylation, chromatin accessibility and expression of small non-coding RNAs.

Data is also available for several chromatin associated factors such as histone modifying enzymes, transcription factors, and components of the core transcriptional machinery.

More of these data type will be included as they become available.

Furthermore, gene expression data for relevant biological samples will also be included in Epigenomics.


The two fundamental types of database records are ‘studies’ and ‘samples’, both of which are assigned unique accession numbers (with prefixes ESS and ESM, respectively).

A study refers to one or more experiments with a common set of scientific aims.

Most often an epigenomic study will correspond to a publication or to a publicly available data set.

For each study, there is a brief summary of the scientific design, together with a listing of the biological samples that were studied and the epigenetic features examined.

Full data source information is provided, including the submitter’s institution, links to the original data submissions in GEO and SRA, links to literature citations in PubMed and (where available) links to the full-text articles in PubMed Central.

Each study is associated with a collection of samples.

A sample corresponds to the biological material that was examined and includes detailed biological source attributes with values drawn from controlled vocabularies.


In order to unify and consistently assign biological attributes, extensive manual curation is performed using submitted meta data as a foundation.

This process may include examining primary literature and researching on-line repositories of cell lines, mouse strains, and tissues.

There are over 20 biological attribute fields available, including strain, ecotype, cultivar, individual, gender, age, developmental stage, cell line, cell type, tissue type, health status and many others.


The Epigenomics database was first released in June of 2010.

As of this writing, it contains 69 studies, 337 samples and over 1100 data tracks from five well-studied species.

Currently, data tracks for global expression of micro and small RNAs (194 tracks), histone modifications (626 tracks), DNA methylation (128 tracks), chromatin accessibility (60 tracks) and various chromatin associated factors (including RNA polymerase, transcription factors, and various histone modifying enzymes) (140 tracks), are available among others.

The entry point for exploring these data is the Epigenomics home page (www.ncbi.nlm.nih.gov/epigenomics/).

In addition to basic database searching functionality, it includes links to a series of tutorial documents that explain how to use the database as well as scientific background documents that cover fundamental topics in epigenetics, such as an introduction to histone modifications.


The Epigenomics database is part of the NCBI’s umbrella Entrez search system, which supports both free-text and fielded queries, together with a uniform system for representing links between related records in different databases [22].

To simplify browsing of the database contents, we have developed a unique Sample Browser tool, which lists samples in a tabular (spreadsheet-like) display.

The user-configurable columns correspond to various biological and experimental attributes while the rows are the epigenomic samples.

The table may be sorted on any column and the entire table may be exported in a spreadsheet-compatible format (comma-separated values).

Pre-set filters provide easy browsing by species, cell type and submitting institution, while a free-text filtering feature allows for fine control of the displayed samples.

Sets of samples may be stored temporarily using a clipboard feature or saved permanently (with a free NCBI login) in named collections.

The Sample Browser also serves as a hub for connecting to other tools that act on sets of samples, specifically, graphical rendering and bulk downloading.


One of the most common tasks performed with track data involves simple inspection of graphical views and several popular genome browsers have been developed for this purpose.

These tools allow peaks at specific features (e.g. promoters and enhancers) to be visualized and compared across different samples.

For example, the differences in the epigenetic marks, H3K4me3 and H3K27me3, at the developmentally regulated NANOG gene locus in both embryonic stem cells and terminally differentiated fibroblasts can be visualized.

The Epigenomics website provides an easy way for users to visualize a chosen set of tracks using either the NCBI Sequence Viewer or the UCSC Genome Browser.

Advanced users may prefer to download track data to their own systems for local analysis.

A bulk downloading tool may be used to retrieve any chosen set of tracks and have them delivered in the form of a compressed archive (ZIP) file containing the corresponding WIG files, together with a ‘read me’ file with further details about the samples.

Track data for Epigenomics is also available for download via an anonymous FTP site.


The Epigenomics database at NCBI has been established to serve as a comprehensive public resource for epigenetic and epigenomic data sets.

Data are being collected from several large scale projects, including the NIH Roadmap Epigenomics project, ENCODE and modENCODE projects as well as from smaller single laboratory studies.

With interest in the field of epigenomics expanding and the amount of data increasing dramatically, it is important that this information be readily available and easily accessed by all members of the scientific community.

Epigenomics introduces the Sample Browser, a new and unique tool at NCBI, which provides an intuitive interface to the database.

We hope to provide users with all levels of knowledge and expertise in the field of epigenetics the ability to examine and analyze these data.


A decade ago, the Gene Expression Omnibus (GEO) database was established at the National Center for Biotechnology Information (NCBI).

The original objective of GEO was to serve as a public repository for high-throughput gene expression data generated mostly by microarray technology.

However, the research community quickly applied microarrays to studies, including examination of genome copy number variation and genome-wide profiling of DNA-binding proteins.

Because the GEO database was designed with a flexible structure, it was possible to quickly adapt the repository to store these data types.

More recently, as the microarray community switches to next-generation sequencing technologies, GEO has again adapted to host these data sets. Today, GEO stores over 20,000 functional genomics studies, and continues to handle the majority of direct high-throughput data submissions from the research community.

Multiple mechanisms are provided to help users effectively search, browse, download and visualize the data at the level of individual genes or entire studies.

This paper describes recent database enhancements, including new search and data representation tools, as well as a brief review of how the community uses GEO data.

GEO is freely accessible at http://www.ncbi.nlm.nih.gov/geo/ [1].

In 2000, the Gene Expression Omnibus (GEO) database was established by the National Center for Biotechnology Information at the National Library of Medicine [1].

GEO was originally built to archive the burgeoning volumes of high-throughput gene expression data beginning to be produced by the research community at that time.


While GEO was originally established to host gene expression data, the database has evolved to host other data types, including comparative genomic analyses, chromatin immunoprecipitation profiling that characterizes genome–protein interactions, non-coding RNA profiling, SNP genotyping and genome methylation status analyses.

Table 1 provides a breakdown of the study types and technologies hosted by GEO.

As microarrays became used routinely in almost every area of biomedical research, the Minimum Information About a Microarray Experiment (MIAME) guidelines were proposed [2].

These guidelines outline the minimum information that should be included when describing a microarray experiment to ensure that the data can be interpreted by others.

In 2002, the Nature journals announced that they supported this proposal and that authors were henceforth required to deposit microarray data in either the GEO or ArrayExpress [3] public repositories so that anyone could freely access and critically evaluate the data discussed in manuscripts [4].

Many other journals similarly adopted this requirement.

It is for this reason that GEO and ArrayExpress have experienced remarkable growth.

Today, GEO archives approximately 20,000 studies comprising 500,000 samples, 33 billion individual abundance measurements, for over 1300 organisms, submitted by 8000 laboratories from around the world and supporting data for over 10,000 published manuscripts.

Since its inception, many aspects of the GEO database and operating procedures have undergone major revisions and development, including: (i) database modifications to support evolving data types, (ii) increasingly stringent submission requirements, concomitant with developing community standards like MIAME, (iii) enhanced submission formats that ease the burden on submitters and promote well-annotated MIAME-compliant data and (iv) improved indexing and analysis tools that help users more easily locate information relevant to their interests.

A timeline summarizing database growth and major developments over the last 10 years, including recent enhancements, is provided in Figure 1.


The overarching design and organization of GEO remains largely unchanged from previous descriptions [5].

Platform, Sample and Series records continue to be the core submitter-supplied objects, while DataSet records represent curated studies that form the basis of GEO’s advanced data display and analysis tools.

Information describing the objects, their content and relationships is provided at http://www.ncbi.nlm.nih.gov/geo/info/overview.html.

A Platform record is composed of a summary description of the array or sequencer and, for array-based Platforms, a data table defining the array template.

A Sample record is composed of a description of the biological material, the experimental protocols to which it was subjected and a data table containing abundance measurements for each feature on the corresponding Platform table.

A Series record defines a set of related Samples considered to be part of a study and describes the overall study aim and design.

We continue to take advantage of the flexible nature of the primary database which facilitates capture of a wide variety of rapidly developing heterogeneous data types.

The tabular data from original submissions are not fully normalized in the database but rather are stored as plain text tab-delimited tables.

While these tables can in principle contain any number of rows and columns, a suite of validation checks performed at the time of upload ensures that they meet basic format and content requirements, which in turn facilitates extraction of core elements to the secondary database.

The validation rules can be easily modified to quickly accommodate novel data types and evolving curation standards.

Corresponding raw data files are linked from each record and stored on FTP servers.

Series summaries, biological sample descriptions, treatments and protocol metadata are stored in specified fields within database tables and have appropriate relations and restrictions.

Unique and stable accession numbers are assigned to each record.


Although the submitter-supplied objects in the primary database are very heterogeneous with regards to content, style and level of metadata detail, the array-based expression data generally share a common set of elements that can be extracted and further processed.

These include: (i) sequence identity tracking information of each feature on the Platform, (ii) normalized expression measurements and (iii) text describing the biological source and experimental aim.

This information is extracted from the submitter-supplied records and organized into an upper-level object called a GEO DataSet.

A DataSet represents a summarized collection of consistently processed experimentally related Sample records categorized according to experimental variables.

GEO Profiles are derived from GEO DataSets.

A GEO Profile consists of the expression measurements for an individual gene across all Samples in a DataSet.

Genes in GEO Profiles are periodically re-annotated according to the latest information in Entrez Gene and UniGene, an important consideration given the dynamic nature of gene annotations.

DataSets and Profiles are a means for transforming diverse styles of incoming data into a relatively standardized format upon which downstream data analysis and data display tools are built.


Much effort continues to be invested into making the deposit process as simple as possible for submitters, while not compromising the quality of the information supplied.

A spreadsheet-based format called GEOarchive has emerged as GEO’s most popular deposit method for both array and next-generation sequence submissions.

GEOarchive spreadsheets are easy to assemble, and enable rapid batch deposit of both large and small studies.

Generic GEOarchive templates are provided, as well as templates tailored for specific data types.

Web-based submission forms are also supported, as are plain text and XML formats.

All these submission methods can capture all components of the MIAME checklist.

The GEO curation team is available to support submitters deposit their data should the need arise.

All submissions are syntactically validated for correct document structure, organization and provision of basic MIAME elements.

Curators also review the content of each submission, aiming to ensure that sufficient information has been supplied to allow general interpretation of the study.

When any problems are identified, such as corrupt files or missing components, a curator will work with the submitter until the issues are resolved.

Despite these checks, it should be noted that submitters have ultimate responsibility for the content and accuracy of their records.

If submitters notice errors or want to make adjustments to their records, they are free to do so at any time using online update tools.

Submitters can keep their records in private status until a manuscript describing the data is published.

Submitters are invited to create a reviewer URL and include it with their manuscript when they send it to journals for review.

This URL allows anonymous, confidential access to the private GEO records cited in the paper, enabling reviewers to download and critically evaluate the data.


GEO accepts next-generation sequence data for studies that examine gene expression, genome–protein interactions, genome methylation and gene regulation.

Submissions are organized similarly to array-based submissions and all standard GEO administrative procedures are applied, full details are provided at http://www.ncbi.nlm.nih.gov/geo/info/seq.html.

GEO hosts the processed and analyzed sequence data, together with sample and study metadata; raw data files are brokered to and linked with NCBI’s Sequence Read Archive (SRA) database [6], ensuring that these sequence data are integrated with NCBI’s collection of sequence-specific resources.

Given the huge volumes and wide scope of experiment types represented in GEO, it is crucial to provide robust tools that allow users to easily locate and access information relevant to their interests.

Queries may be performed at the GEO DataSets and GEO Profiles search interfaces (http://www.ncbi.nlm.nih.gov/gds and http://www.ncbi.nlm.nih.gov/geoprofiles, respectively).

A simple keyword search is often sufficient to locate relevant data.

However, GEO data are extensively indexed under many separate fields, meaning that users can refine their search by constructing fielded queries.

Advanced Search pages have recently been developed for the GEO DataSets and GEO Profiles databases.

These pages greatly assist users to construct complex multi-part fielded search statements.

Alternatively, users can write and execute their own search statements directly in the search boxes.

A full listing of all indexed fields, with examples and tips, is provided at http://www.ncbi.nlm.nih.gov/geo/info/qqtutorial.html.

Examples of indexed fields include DataSet Type, Gene Symbol, Gene Ontology (GO) terms, Chromosome and Base Position.


The search fields are not restricted to descriptive metadata, they can also be used to locate genes flagged as having interesting gene expression patterns.

For example, to find genes that exhibit differential expression with respect to age or development stage in mouse, users could query GEO Profiles with: (age[Flag Information] OR development stage[Flag Information]) AND mouse[Organism].

Additional query approaches are provided on GEO DataSet records to help users locate genes of interest.

These include a tool that allows the user to perform t-tests with varying levels of significance on selected sets of Samples, as well as pre-computed cluster heatmap images that may be queried for specific genes.

Sequence-based queries may be performed using the GEO BLAST interface.

The DataSet Browser is located at http://www.ncbi.nlm.nih.gov/sites/GDSbrowser.

Complete GEO records and raw data files are freely available for bulk download from the GEO FTP site.

High-throughput file transfer through Aspera Connect is also supported.

Data are available in multiple formats including tab-delimited tables, plain text and XML.

Raw data files are provided in native format where possible.

Additional mechanisms are provided to enable download of more focused sets of data.

For example, URLs can be constructed to retrieve metadata files for specific records and annotated gene expression values can be downloaded from GEO Profiles results pages.

Download options are detailed at http://www.ncbi.nlm.nih.gov/geo/info/download.html.

Programmatic access to metadata is supported through a suite of NCBI programs called the Entrez Programming Utilities.

These tools allow users to develop pipelines that locate and download data of interest.


Various graphical renderings are provided to help users identify data of interest.

GEO Profile charts portray the expression behavior of one gene across all Samples in a DataSet.

The charts present several categories of information including expression measurement values, expression measurement rankings and an outline of the experimental design and variables.

A user can look at a chart and quickly assess whether gene activity is shifting according to experimental conditions.

Display of entire DataSets is facilitated through several types of precomputed cluster heatmaps.

These clusters are interactive in that the user can search for specific genes, or highlight specific areas using a moveable selector box, and then download those regions as a text file or link to corresponding GEO Profiles.

Next-generation sequence data presents new challenges for data display.

Hundreds of GEO ChIP-seq and RNA-seq Samples have been selected for re-processing by NCBI’s new Epigenomics resource [7] at http://www.ncbi.nlm.nih.gov/epigenomics.

Once migrated to the Epigenomics resource, these data undergo advanced curation and are made available for review as precomputed tracks on genome browsers.

Extensive internal and external links are applied to GEO data.

Internal links help the user discover related gene expression Profiles.

For example, Profile Neighbor links retrieve genes that show a similar gene expression pattern to the chosen Profile within a DataSet, Chromosome Neighbor links connect genes that are physically close to each other on the chromosome to help investigate gene expression neighborhoods, and Sequence Neighbor links gather Profiles from across all DataSets that are related by nucleotide sequence similarity.

Inter-database links provide reciprocal connections to corresponding information in other NCBI databases including Gene, PubMed and GenBank [8].


As the number of GEO data sets increases, so does the opportunity to aggregate and use the data in a much broader context than that for which it was originally intended.

GEO typically receives over 40,000 web hits and 10,000 bulk FTP downloads per day.

A review of the literature reveals over 1000 publications in which researchers describe how they applied data they found in GEO to their own studies, see http://www.ncbi.nlm.nih.gov/geo/info/ucitations.html.

Many of these publications report how GEO data were used to verify gene expression signatures of individual genes in specific cell types or under specific conditions, providing evidence to support conclusions in the authors’ own study [9].

Other authors incorporate GEO data into their own analyses, such as using GEO data as controls or training sets within their own study [10].

The huge volumes of data present bioinformaticians with a wealth of test material for developing new statistical algorithms [11] or analysis strategies, including modeling gene networks, predicting gene function and regulation [12] and identifying disease and toxicity predictors [13].

Others have attempted to define systems-wide relationships and maps between GEO expression data and other data types such as gene pathways [14,15] and ontologies [16].

Many subject-specific and/or added-value databases have been created using GEO data, targeting specific audiences, or presenting the data in alternative formats [17–19].

Despite the heterogeneity of data types and experimental designs, many users have been able to perform powerful meta-analyses across thousands of independently-submitted GEO Samples [20,21].

It can be anticipated that as public high-throughput functional genomic data sets continue to accumulate, third-party users will exploit these data in ever-more innovative ways.


Over the past decade, NCBI’s GEO database has served as the major public archive for high-throughput microarray- and sequence-based functional genomic data sets.

Aside from archiving, cross-linking and making vast amounts of data freely available for download, GEO also provides several user-friendly web-based tools and strategies to assist users query and analyze these data.

While GEO represents a unifying resource for thousands of valuable public functional genomics studies, significant challenges remain in achieving maximum benefit from these data.

Those challenges include enabling better integration and cross-comparison of diverse data sets, data types and data resources and correlating the data with phenotype information.

GEO will continue to be developed and refined towards achieving these goals, as well as improving the accessibility and usability of the data for as broad an audience as possible.


Aspirin is the most widely used drug for the prevention of cardiovascular events.

However, several studies have reported a considerable inter-individual variation in the platelet response to aspirin.

Importantly, high residual platelet reactivity corre- lates with an increased risk of recurrent cardiovascular events [1,2].

Insufficient platelet inhibition is likely to be explained by multiple mechanisms [3], including an increased platelet turnover [4-7], yielding a larger population of immature, reticulated platelets.

Recent studies have suggested that indi- viduals with an increased platelet turnover may have a reduced response to antiplatelet drugs [4-7].


Circulating platelets are heterogeneous with respect to size and density, and large, dense platelets constitute a highly reactive subpopulation [8-10].

When platelet turnover is high, the proportion of large, reactive platelets released from the bone marrow megakaryocytes increases [10,11].

Newly pro- duced platelets are capable of producing membrane and secretory proteins because of significant amounts of mega- karyocyte-derived mRNA.

The rate of platelet turnover can be quantified using flow cytometry with fluorescent RNA-staining dyes [12,13].


The response to antiplatelet therapy is probably affected by the ability of young platelets to produce proteins including the final common pathway of platelet aggregation, glycoprotein IIb/IIIa, cyclooxygenase (COX)-2 and alpha-granule proteins, such as fibrinogen and von Willebrand factor [5,14,15].

Furthermore, the increase in platelet turnover indicated by the presence of immature platelets may be important per se, as platelets unaffected by drugs are introduced into the blood stream possibly causing the overall platelet inhibition to be insufficient [4].


Recently, we have demonstrated that immature platelet levels are increased in patients with acute coronary syndromes [16].

The study showed a trend towards increased levels of immature platelet levels in patients with coronary artery disease (CAD) and type 2 diabetes.


In the present study, we aimed to investigate the relationship between platelet aggregation, platelet activation and platelet turnover in a population of stable CAD patients including a high proportion of diabetics.


As previously described, a total of 177 patients with stable CAD were recruited from the Western Denmark Heart Registry from November 2007 to April 2008 [17,18].

Among 727 invited patients, 423 did not want to participate and 127 were either not eligible or were excluded according to the criteria given below.


In this observational study, patients were eligible for inclusion if they were above 18 years of age and had significant CAD verified by coronary angiography.

Our aim was to provide a study population of stable CAD patients with a relatively high-risk profile.

The study population included about 50% diabetics and a high prevalence of patients with previous myocardial infarction.

All diabetic patients had been diagnosed with type 2 diabetes and were treated with oral antidiabetic agents and/or insulin.

In the remaining patients, fasting glucose was measured to exclude undiagnosed diabetes (glucose ‡ 7 mM) or impaired glucose tolerance.


Patients were excluded if they had had any ischemic event or revasculariza- tion procedure within the previous 12 months, if their platelet count was < 120 · 10 9 L )1 or if they took warfarin or any drug known to affect platelet function [e.g. clopidogrel, non- steroidal anti-inflammatory drugs (NSAIDs), ticlopidin, dipy- ridamol].

All patients were taking 75 mg of plain aspirin daily.


The study was conducted in agreement with the Helsinki-II- declaration and was approved by The Central Denmark Region Committees on Biomedical Research Ethics (M- 20070180).

All participants gave written informed consent.


To optimize compliance and uniform pharmacokinetics all patients received a pill-dispensing box with seven tablets of 75- mg non-enteric coated aspirin (Hjerdyl Ò ; Sandoz, Copenhagen, Denmark) in separate compartments for each day of the week.

Compliance was further optimized by face-to-face interviews and pill counting and was confirmed by measurements of serum thromboxane B 2 .


Standardized blood sampling was performed 1 h after aspirin intake.

Samples were collected using a large bore needle (19-G) and a minimum of stasis with patients in the supine position after 30 min of rest in sitting position.

The first milliliters of blood were collected in tubes without anticoagulant for separation of serum.

Samples for analyzes of platelet aggregation and platelet characteristics were collected in tubes containing 3.2% citrate and EDTA, respectively (Terumo, Leuven, Belgium).


These variables were all measured using a XE-2100 hematology analyzer (Sysmex, Kobe, Japan) allowing flow cytometric detection of immature platelets as previously described [12,16].

Briefly, platelet RNA was stained with fluorescent dyes (polymethine and oxazine) before stained cells were passed through a semiconductor diode laser beam.

The resulting fluorescence intensity (RNA content) and forward light scatter (cell volume) were measured, and a preset gate (XE IPF Master software) (Sysmex) discriminated between mature and imma- ture platelets.

Absolute immature platelet counts (IPC) were obtained and the immature platelet fraction (IPF) was calculated as the ratio of immature platelets to the total platelet count and is given in percent.

The method for platelet count determination has previously been described [16].

Platelet size parameters were derived from the platelet size distribution.

Mean platelet volume (MPV) was calculated by dividing the platelet crit by platelet impedance count.

Platelet distribution width (PDW), a measure of platelet anisocytosis, is the width of the size distribution curve in femtoliters (fL) at the 20% level of the peak.

The platelet large cell ratio (P-LCR) is the number of cells falling above the 12-fL threshold divided by platelet count.

Samples from all study participants were measured on the same Sysmex XE-2100 apparatus and were analyzed within 90 min of collection.


Platelet aggregation was evaluated according to manufacturerÕs instructions using two different point-of-care tests; multiple electrode aggregometry (MEA) and the VerifyNow Ò (Accu- metrics, San Diego, CA, USA) Aspirin test.

All analyzes were performed within 2 h of sampling.


Platelet aggregation assessed by MEA was performed using an impedance aggregometer (Multiplate Ò ; Dynabyte, Mu¨nich, Germany).

Aggregation was induced by final agonist concen- trations of arachidonic acid (AA) 1.0 mM (Medinova Scientific, Glostrup, Denmark), ADP 10 lM (Sigma-Aldrich, Broendby, Denmark) and collagen 1.0 lg mL )1 (Collagen Reagent Horm; Nycomed, Linz, Austria).

Aggregation was recorded for 6 min and reported as the area under the curve (AUC, Aggregation Units · minutes) [19].


The VerifyNow Ò Aspirin system (Accumetrics) is based on turbidimetric optical detection of platelet aggregation in whole blood using AA as agonist [20].

The instrument measures light transmittance, reported as Aspirin Reaction Units.


Platelet activation was determined by analysis of soluble P- selectin (sP-selectin) and thrombopoietin, the main regulator of platelet production from the bone marrow, was measured to investigate determinants of platelet turnover.

Whole blood was allowed to clot for 30 min at room temperature, before serum was separated by centrifugation at 1000 · g for 15 min and stored at )80°C until analysis.

Soluble P-selectin and throm- bopoietin concentrations were determined by ELISA kits according to the manufacturerÕs instructions (R&D Systems Europe, Abingdon UK).

Measurements of serum thromboxane B 2 were performed according to Patrono et al. [ 19] with the modifications that serum was collected from whole blood by centrifugation (10 min at 2600 · g) after 1 h of clotting at 37°C, and an ELISA was used (Cayman chemicals, Ann Arbor, MI, USA).

All samples were measured in duplicate and in two dilutions.

Samples with results outside the standard curve were re-assayed with appropriate dilutions.


Continuous variables are expressed as mean ± SD if normally distributed and as median (25th, 75th percentile) if not.

Distributions of categorical variables were compared with the v 2 -test and presented as absolute counts and percentages.

All variables were tested for normality using the DÕAgostino- Pearson normality test.

Continuous variables were analyzed as appropriate using an unpaired t-tests or the Mann-Whitney U-test, except that the Kruskal-Wallis test was used, when more than two groups were compared.

Correlations were calculated using SpearmanÕs rank correlation coefficient.