in 2006 , epstein et al . reported that hypomagnesaemic hypoparathyroidism could be caused by long - term use of a proton - pump inhibitor ( ppi ) , omeprazole 1 .
thereafter , case reports accumulated , in which ppis were shown to be associated with hypomagnesaemia 2 - 11 , and in 2011 , the us food and drug administration ( fda ) published a safety announcement that long - term use of ppis can lead to hypomagnesaemia 12 . although recognized as a rare side effect of ppis , hypomagnesaemia is a serious condition that can be complicated by life - threatening arrhythmias and neurologic manifestations 10 , 11 .
exactly how ppis could cause hypomagnesaemia has not been clarified , and controlled studies are required to delineate the mechanisms 13 .
symptoms include tetany , seizures , muscle cramps , vomiting , nausea , and diarrhea , but these are not always found in patients with hypomagnesaemia 10 , 11 .
most reports on ppi - induced hypomagnesaemia concern omeprazole or esomeprazole , the s - isomer of omeprazole , but the recurrence after substitution by other ppis suggests that this is a class effect commonly found for ppis .
the present study was performed to assess omeprazole and esomeprazole in terms of susceptibility to hypomagnesaemia , and to this end , more than a million case reports on adverse drug events submitted to the fda database were reviewed .
input data for this study were taken from the public release of the data in the fda 's adverse event reporting system ( aers ) , which covers the period from the first quarter of 2004 through the end of 2009 .
this database relies on spontaneous reports of adverse drug events by health professionals , consumers , and manufacturers .
the data structure of aers is in compliance with international safety reporting guidance ich e2b issued by the international conference on harmonisation , consisting of 7 data sets : patient demographic and administrative information ( demo ) , drug / biologic information ( drug ) , adverse drug events ( reac ) , patient outcomes ( outc ) , report sources ( rpsr ) , drug therapy start and end dates ( ther ) , and indications for use / diagnosis ( indi ) .
the adverse drug events in reac are coded using preferred terms ( pts ) in the medical dictionary for regulatory activities ( meddra ) terminology .
prior to analysis , all drug names were unified into generic names by a text - mining approach , because aers permits the registering of arbitrary drug names , including trade names and abbreviations .
spelling errors were detected by a spell checker software , gnu aspell , and carefully confirmed by working pharmacists .
foods , beverages , treatments ( e.g. x - ray radiation ) , and unspecified names ( e.g. beta - blockers ) were omitted for this study , and the total number of omissions was 164,384 . finally , duplicated reports were deleted according to the fda 's recommendation of adopting the most recent case number , resulting in a reduction in the number of reports from 2,231,029 to 1,644,220 .
a total of 22,017,956 co - occurrences were found in 1,644,220 reports , where a co - occurrence was a pair of a drug and an adverse drug event . in pharmacovigilance analyses
, data mining algorithms have been developed to identify an association between a drug and an adverse drug event or a drug - associated adverse drug event as a signal that is reported more frequently than expected by estimating expected reporting frequencies on the basis of information on all drugs and all adverse drug events in a database 14 - 20 .
for example , the proportional reporting ratio ( prr ) 14 , the reporting odds ratio ( ror ) 15 , the information component ( ic ) 16 , and the empirical bayes geometric mean ( ebgm ) 17 are widely used .
indeed , the prr is currently used by the uk medicines and healthcare products regulatory agency ( mhra ) , the ror by the netherlands pharmacovigilance centre , the ic by the world health organization ( who ) , and the ebgm by the fda .
all of these algorithms extract decision rules for signal detection and/or calculate scores to measure an association between a drug and an adverse drug event from a two - by - two frequency table of counts that involve the presence or absence of a particular drug and a particular adverse drug event occurring in case reports .
these algorithms , however , differ from one another in that the prr and ror are frequentist ( non - bayesian ) ones , whereas the ic and ebgm are bayesian ones . in this section ,
only the scoring thresholds used in the present study are given , and the reader is referred to review articles for more extensive details of each statistical test 18 - 20 . in this section ,
we define how the association between a drug and an adverse drug event is classified as a signal , when using each statistical test . using the prr ,
a signal is detected if the count of co - occurrences is 3 or more , and the prr is 2 or more with an associated  value of 4 or more 14 . for the ror ,
a signal is detected if the lower bound of the 95% two - sided confidence interval of ror exceeds 1 15 .
signal detection using the ic is done using the ic025 metric , a criterion indicating the lower bound of the 95% two - sided confidence interval of the ic , and a signal is detected if the ic025 value exceeds 0 16 . finally , the eb05 metric , a lower one - sided 95% confidence limit of ebgm 17 , is used and a signal is detected when eb05 is greater than or equal to the threshold value 2 . in this study ,
the adverse drug events coded by pt numbers were listed as omeprazole- and esomeprazole - associated , when at least 1 of 4 indices met the criteria indicated above , and subsequently hypomagnesaemia was identified by the pt code number 10021027 .
input data for this study were taken from the public release of the data in the fda 's adverse event reporting system ( aers ) , which covers the period from the first quarter of 2004 through the end of 2009 .
this database relies on spontaneous reports of adverse drug events by health professionals , consumers , and manufacturers .
the data structure of aers is in compliance with international safety reporting guidance ich e2b issued by the international conference on harmonisation , consisting of 7 data sets : patient demographic and administrative information ( demo ) , drug / biologic information ( drug ) , adverse drug events ( reac ) , patient outcomes ( outc ) , report sources ( rpsr ) , drug therapy start and end dates ( ther ) , and indications for use / diagnosis ( indi ) .
the adverse drug events in reac are coded using preferred terms ( pts ) in the medical dictionary for regulatory activities ( meddra ) terminology .
prior to analysis , all drug names were unified into generic names by a text - mining approach , because aers permits the registering of arbitrary drug names , including trade names and abbreviations .
spelling errors were detected by a spell checker software , gnu aspell , and carefully confirmed by working pharmacists .
foods , beverages , treatments ( e.g. x - ray radiation ) , and unspecified names ( e.g. beta - blockers ) were omitted for this study , and the total number of omissions was 164,384 . finally , duplicated reports were deleted according to the fda 's recommendation of adopting the most recent case number , resulting in a reduction in the number of reports from 2,231,029 to 1,644,220 .
a total of 22,017,956 co - occurrences were found in 1,644,220 reports , where a co - occurrence was a pair of a drug and an adverse drug event .
in pharmacovigilance analyses , data mining algorithms have been developed to identify an association between a drug and an adverse drug event or a drug - associated adverse drug event as a signal that is reported more frequently than expected by estimating expected reporting frequencies on the basis of information on all drugs and all adverse drug events in a database 14 - 20 .
for example , the proportional reporting ratio ( prr ) 14 , the reporting odds ratio ( ror ) 15 , the information component ( ic ) 16 , and the empirical bayes geometric mean ( ebgm ) 17 are widely used .
indeed , the prr is currently used by the uk medicines and healthcare products regulatory agency ( mhra ) , the ror by the netherlands pharmacovigilance centre , the ic by the world health organization ( who ) , and the ebgm by the fda .
all of these algorithms extract decision rules for signal detection and/or calculate scores to measure an association between a drug and an adverse drug event from a two - by - two frequency table of counts that involve the presence or absence of a particular drug and a particular adverse drug event occurring in case reports .
these algorithms , however , differ from one another in that the prr and ror are frequentist ( non - bayesian ) ones , whereas the ic and ebgm are bayesian ones . in this section ,
only the scoring thresholds used in the present study are given , and the reader is referred to review articles for more extensive details of each statistical test 18 - 20 . in this section , we define how the association between a drug and an adverse drug event is classified as a signal , when using each statistical test . using the prr , a signal is detected if the count of co - occurrences is 3 or more , and the prr is 2 or more with an associated  value of 4 or more 14 . for the ror
, a signal is detected if the lower bound of the 95% two - sided confidence interval of ror exceeds 1 15 .
signal detection using the ic is done using the ic025 metric , a criterion indicating the lower bound of the 95% two - sided confidence interval of the ic , and a signal is detected if the ic025 value exceeds 0 16 . finally , the eb05 metric , a lower one - sided 95% confidence limit of ebgm 17 , is used and a signal is detected when eb05 is greater than or equal to the threshold value 2 . in this study ,
the adverse drug events coded by pt numbers were listed as omeprazole- and esomeprazole - associated , when at least 1 of 4 indices met the criteria indicated above , and subsequently hypomagnesaemia was identified by the pt code number 10021027 .
the total number of co - occurrences with omeprazole and esomeprazole was 178,766 and 121,506 , representing 0.812% and 0.552% of all co - occurrences in the database , respectively . in total , 818 and 743 adverse drug events were listed as omeprazole- and esomeprazole - associated with 55,904 and 48,481 co - occurrences , respectively .
hypomagnesaemia ranked 85 among 818 omeprazole - associated adverse drug events , and 135 among 743 for esomeprazole .
the statistical data on omeprazole- and esomeprazole - associated hypomagnesaemia are listed in table 1 .
an association with hypomagnesaemia was suggested for both ppis , but the association was more noteworthy for omeprazole .
magnesium is an essential factor implicated in many biochemical and physiological processes , and its homeostasis is sophisticatedly regulated by intestinal absorption , renal excretion and other systems in the body 10 , 11 .
hypomagnesaemia or hypermagnesaemia may arise from various types of disorders 10 , 11 . in 2006 ,
in which a ppi , omeprazole , was shown to be associated with hypomagnesaemia 1 . to date ,
about 10 case reports have been published with respect to ppi - associated hypomagnesaemia 2 - 9 , and their findings can be summarized as ; 1 ) ppi long - term use was observed in patients with hypomagnesaemia , 2 ) symptoms did not occur until plasma concentrations were less than 0.5 mmol / l , 3 ) mechanisms by which the hypomagnesaemia occurred under ppi therapy remain unclear , 4 ) hypokalaemia often accompanied the hypomagnesaemia , 5 ) hypocalcaemia also frequently developed via impairment of parathyroid hormone secretion , 6 ) oral or parenteral supplement of magnesium was effective for temporary relief from symptoms , but unable to correct the plasma concentration of magnesium , and 7 ) withdrawal of ppi allowed to resolve the hypomagnesaemia 10 , 11 .
hypomagnesaemia is understood to be a rare side effect of ppis , but epstein et al .
hypomagnesaemia might be underdiagnosed , in part , due to the relatively low frequency of magnesium measurements in routine clinical analysis .
if hypomagnesaemia is found in ppi users , it might be attributed to co - administered diuretics or other nephrotoxic drugs .
it is important to perform clinical studies to clarify the true prevalence and risk factors , and to clarify the mechanisms by which hypomagnesaemia develops . to date , most case reports on ppi - associated hypomagnesaemia concern omeprazole or esomeprazole , but hypomagnesaemia is understood to be common for ppis .
broeren et al . showed that hypomagnesaemia was resolved after the replacement of omeprazole with a h2-blocker , ranitidine , but the re - replacement of ranitidine with pantoprazole resulted in recurrence 5 .
they also documented another case in which the replacement of omeprazole with rabeprazole resulted in a further decrease in serum levels of magnesium 8 . in this study , using 1,644,220 reports from 2004 to 2009 , it was suggested that hypomagnesaemia was associated with omeprazole and esomeprazole , and was more noteworthy for omeprazole , suggesting the usefulness of the aers database and official pharmacovigilance tools .
although pantoprazole , lansoprazole and rabeprazole were also analyzed , the numbers of co - occurrences were not large enough to detect signals .
the first clinical report on ppi - associated hypomagnesaemia appeared in late 2006 , which was on omeprazole and esomeprazole , and the ppi - associated hypomagnesaemia entered clinical consciousness slowly .
the aers data used in this study were those from 2004 to 2009 , and the latest data should be used to assess the associations with pantoprazole , lansoprazole and rabeprazole . the aers database is considered a valuable tool ; however , some limitations inherent to spontaneous reporting have been pointed out 18 .
first , the data occasionally contain misspelling and miswords , although the structure of aers is in compliance with the international safety reporting guidance .
second , the system was started more than 10 years ago , and reporting patterns have changed over time .
third , the adverse events are coded using hierarchical terms of pts of meddra , and changes in terminology over time also might affect the quality of the database .
to overcome problems with data quality , we manually corrected mistakes in the data entities and deleted duplicates according to fda 's recommended method , resulting in the development of a novel system to analyze an association between a drug and an adverse drug event .
previously , this system has been used to assess adverse drug events accompanying the use of platinum agents 21 .
the data obtained was consistent with clinical observations , suggesting the usefulness of the system 21 .
additionally , this system was used to evaluate susceptibility to hypersensitivity reactions for 14 anticancer agents , and it was found that the number of co - occurrences was an important factor in signal detection 22 , 23 . very recently , this system was applied to the evaluation of adverse drug events induced by statins 24 , capecitabine 25 and tigecycline 26 , and again the reproducibility of clinical observations was suggested , providing that the number of co - occurrences was large enough to detect a signal .
it should be noted that there is no credible counterfactual means , e.g. , a randomized control group , to identify an association between a drug and an adverse drug event as a signal , and therefore disease - oriented adverse events can be extracted as signals .
for example , hypomagnesaemia was extracted as an omeprazole - associated adverse drug event , but might be common in patients with acid peptic disorders irrespective of the administration of ppis .
generally , the results obtained using this system can be biased by unmeasured confounding factors , and flawed by incomplete data ; however , a comparison among ppis possibly offsets them , resulting in a rank - order of association according to the statistical metrics . in conclusion , the data obtained in this study do not provide sufficient evidence to recommend systematic monitoring of magnesium levels in plasma , but chronic exposure to a ppi can lead to severe hypomagnesaemia .
 objective : case reports showing that proton - pump inhibitors ( ppis ) , omeprazole and esomeprazole , can cause hypomagnesaemia have been accumulating since 2006 . in this study , 
 the reports submitted to the adverse event reporting system ( aers ) of the us food and drug administration ( fda ) were evaluated to assess omeprazole and esomeprazole in terms of susceptibility to hypomagnesaemia.methods : after a revision of arbitrary drug names and the deletion of duplicated submissions , the reports involving omeprazole and esomeprazole were analyzed . 
 standardized official pharmacovigilance tools were used for the quantitative detection of a signal , i.e. , an association between a drug and an adverse drug event , including the proportional reporting ratio , the reporting odds ratio , the information component given by a bayesian confidence propagation neural network , and the empirical bayes geometric mean.results : a total of 22,017,956 co - occurrences were found in 1,644,220 reports from 2004 to 2009 , where a co - occurrence was a pair of a drug and an adverse drug event . in total , 
 818 and 743 adverse drug events were listed as omeprazole- and esomeprazole - associated , with hypomagnesaemia ranking 85th and 135th , respectively . 
 although both ppis were associated with hypomagnesaemia , the statistical metrics suggested that the association was more noteworthy for omeprazole.conclusion : the data obtained in this study do not provide sufficient evidence to recommend systematic monitoring of magnesium levels in plasma , but chronic exposure to a ppi can lead to severe hypomagnesaemia . 
Introduction
Methods
Data sources
Data mining
Results
Discussion