No increase in new users of blood glucose-lowering drugs in Norway 2006-2011: a nationwide prescription database study

Hanne Strøm
Randi Selmer
Kåre I Birkeland
Henrik Schirmer
Tore Julsrud Berg
Anne Karen Jenum
Kristian Midthjell
Christian Berg
Lars Christian Stene
No increase in new users of blood glucose-lowering drugs in Norway 2006-2011: a nationwide prescription database study
R E S E A R C H A R T I C L E Open Access
Background: National estimates for the occurrence of diabetes are difficult to obtain, particularly time trends in incidence. The aim was to describe time trends in prevalent and incident use of blood glucose-lowering drugs by age group and gender in Norway during 2005-2011. Methods: Data were obtained from the nationwide Norwegian Prescription Database. We defined prevalent users of "insulins only" as individuals having no oral antidiabetic drugs (OAD) dispensed from a pharmacy during the previous 24 months or in the subsequent 12 months. Incident users had no blood glucose-lowering drugs dispensed in the previous 24 months; incident "insulins only" users also had no OAD in the subsequent 12 months. Results: In 2011, 3.2% of the population had blood glucose-lowering drugs dispensed, and the incidence rate was 313 per 100,000 person years. The prevalence of OAD use increased from 1.8% in 2005 to 2.4% in 2011; however a decreasing trend in incidence of OAD use was observed, particularly in those aged 70 years and older. In 2010, 0.64% of the population had insulins only dispensed, with an overall incidence rate in the total population of 33 per 100,000 person years which was stable over time.Conclusions: In this nationwide study, we found that although the prevalent use of OAD had increased in recent years, incident use was stable or had decreased. This may indicate that the increase in diabetes occurrence in Norway is levelling off, at least temporarily.

Background

Diabetes constitutes a major public health challenge and numerous studies suggest that the prevalence is increasing in most countries [1]. However, reliable national estimates for the occurrence of diabetes are difficult to obtain, particularly time trends in incidence. Population-based health studies have limitations, since study samples tend to have low and therefore potentially biased participation [2,3]. A few countries such as Denmark, Sweden and Scotland have established national diabetes registers to monitor trends in the occurrence of diabetes [4,5], but few of these have complete coverage.

While most approaches to estimating the total number with diagnosed diabetes in a population are likely to have important sources of error, data from nationwide prescription drug databases can overcome the problems with biased participation and limited sample sizes to estimate gender-and age-specific trends. All diagnosed patients with Type 1 diabetes and a large majority of patients with Type 2 diabetes in most Western countries are treated with blood glucose-lowering drugs. In European children, insulin use essentially indicates a diagnosis of Type 1 diabetes [6].

Blood glucose-lowering drugs are used for other indications than diabetes, e.g. metformin for the treatment of polycystic ovary syndrome (PCOS) and this has to be taken into account. Furthermore, diabetes not treated with blood glucose-lowering drugs and undiagnosed diabetes will not be covered but important trends can nevertheless be monitored with high quality population-based prescription databases [7].

The primary aim of this study was to describe time trends in prevalent and incident use of blood glucose-lowering drugs by age group and gender in Norway during 2005-2011.

Methods

Data sources

The Norwegian Prescription Database (NorPD) was established in 2004 and includes all prescriptions redeemed by individual patients (encrypted) at pharmacies. Information on drug use in hospitals and nursing homes is collected but not on an individual level. NorPD covers drug supply for the entire population of Norway (4.92 million inhabitants in 2011). All pharmacies are obliged by law to report data electronically to the NorPD each month [8]. Blood glucose-lowering drugs are classified in the Anatomical Therapeutic Chemical (ATC) classification system in group A10. Insulins and analogues are classified in A10A, while other blood glucose-lowering drugs, referred to here as oral antidiabetic drugs (OAD), are classified in A10B [9,10].

Research was part of the mandate sanctioned by the Norwegian Data Protection Authority when the NorPD was established. Permission to conduct this descriptive study was therefore not required from the Regional Committee for Medical Research Ethics.

Study population

The size of the population by gender and age in each calendar year studied was obtained from Statistics Norway. The analyses were based on data from NorPD about drugs prescribed and dispensed from 1 January 2004 to 31 December 2011. Data from 2004 were included to allow for a run-in period. Treatment with blood glucoselowering drugs was used as a proxy for drug-treated diabetes. Figure 1 illustrates the definitions used for classifying subjects as (a) prevalent and (b) incident users of any blood glucose-lowering drug, OAD and insulins only.

Outcome measures

(a) Prevalence outcome measures

Individuals with at least one prescription of any blood glucose-lowering drug dispensed in a calendar year were counted as prevalent users. Individuals with at least one OAD prescription alone or in combination with insulins dispensed in a calendar year were counted as prevalent users of OAD. Individuals with at least one prescription of insulins dispensed in a calendar year but no OAD in the periods 24 months prior to or 12 months after their first insulin prescription was dispensed were counted as prevalent users of insulins only.

(b)Incidence outcome measures Incident users of blood glucose-lowering drugs were defined as individuals having at least one prescription of these drugs dispensed in a calendar year but not in the 24 months period prior to the first prescription. Incident users of OAD were defined as individuals having at least one prescription of OAD dispensed in a calendar year or period but no blood glucose-lowering drugs in the previous 24 months. Incident users of insulins only were defined as individuals having at least one prescription of insulin dispensed in a calendar year or period but no blood glucose-lowering drugs in the previous 24 months, and no OAD in the subsequent 12 months.

Statistical methods

Person years for calculation of incidence rates were estimated from the mean population size for each gender, age group and calendar year. Influence of age and gender and test for trend by calendar year were modelled by Poisson regression using STATA version 13 (StataCorp LP, College Station, TX, USA). Incidence rate ratio (IRR) per year was calculated, and interaction between calendar year and age was tested by likelihood ratio test.

Results

Prevalent use of blood glucose-lowering drugs

In 2011, 156,540 individuals (3.2% of the population) had blood glucose-lowering drugs dispensed compared to 117,541 individuals (2.5% of the population) in 2005. As expected, the prevalence increased strongly with age.

The prevalence was higher in men than in women after 40 years of age. The peak prevalence in men was at age 76 years (12.4%) and in women at age 80 years (9.9%), a pattern very similar to that of users of OAD ( Figure 2).

The prevalence of OAD use increased from 1.8% in 2005 to 2.4% in 2011 and there was an increase in all age groups, with a larger increase in men than in women ( Figure 3).

In 2010, 31,515 individuals (0.64% of the population) had insulins only dispensed and the prevalence was stable during the period 2006-2010 (data not shown). In a sensitivity analysis we calculated the prevalent users of insulins only by extending the period without previous use of OAD up to six years. With a six year period, the number declined to 27,927 (0.57%) (Additional file 1: Table S1).

Incident use of blood glucose-lowering drugs

The incidence of OAD use was significantly higher in women than in men aged 20-39 years and the pattern was reversed after 40 years of age ( Figure 4a). In 2011, the overall incidence rate was significantly lower than the previous year (  (Figures 5a-b). In women, the highest incidence of insulin use only was in the 30-39 year age group, and for men it was highest among the 70-79 year age group (Figure 4b).

In a sensitivity analysis, 59% of incident insulins only users among women aged 30-39 years in 2008 had stopped insulin treatment within the following two years, indicating gestational diabetes rather than Type 1 diabetes (Additional file 2: Figure S1). There was no significant overall time trend in incident use of insulins only for men (IRR per year = 1.01 (95% CI (0.98-1.03) but a weak increasing overall trend for women was observed (IRR per year = 1.03 (1.01-1.05) (Figures 5c-d).

Supplementary material

Detailed data tabulated by age group, gender and calendar year are provided in Additional file 1: Table S2-S5. Population sizes used to calculate incidence and prevalence are shown in Additional file 1: Table S6.

Discussion

We have monitored the use of blood glucose-lowering drugs in Norway, and found an increase in the overall prevalence from 2.5% in 2005 to 3.2% in 2011. Interestingly, the number of new users was, for the first time in the period, significantly lower in 2011 than the year before and this was primarily due to a reduction in incident use of OAD in the 70 years and older age groups.

The prevalence of diabetes depends on the incidence and mean duration of the disease, both of which are affected by several factors. For example, increased survival among those with the diagnosis will increase the prevalence [11] and higher life expectancy (ageing) in the general population could influence the incidence. Others have shown that declining mortality among patients may contribute, but that increasing incidence was the main driver of the increasing prevalence over time, at least in Denmark 1992-2003 [7] and in Scotland 1993-2004 [5]. We did not have mortality data in our study but others have shown a secular decrease in mortality from ischaemic heart disease among patients with diabetes in Norway [12], suggesting that this could have contributed to the observed increasing prevalence with concurrent decline in incidence.

The strength of this study is the complete coverage of the Norwegian population [8]. NorPD should therefore provide good estimates for the number of patients with diabetes treated with blood glucose-lowering drugs (100% reimbursed), with a few caveats that are discussed below. The study does not cover diabetes that is not treated with blood glucose-lowering medication, and classification of diabetes into Type 1 or Type 2 diabetes based on available data is difficult in the older age groups. Individuals with at least one prescription of blood glucose-lowering drugs dispensed were included but the vast majority had actually had several prescriptions. In 2010, of incident users in both groups (insulins only and OAD) recorded, 88% redeemed more than one prescription during the following 12 months. Institutionalisation, emigration, switch to lifestyle treatment only, or off-label use (PCOS, obesity) could partly confound the results (see below). The high incident use of insulins in women in reproductive age might be explained by gestational diabetes. Another limitation is lack of individual level data of institutionalised patients with diabetes. About 13% of the population above 80 years of age (approximately 30,000 people) lived in nursing homes in Norway in 2011 (figures from Statistics Norway, www.ssb.no). Thus, the prevalence and incidence in the oldest patient groups will be underestimated. For instance, recalculating for users of blood glucose-lowering drugs in 2011, the prevalence increased from 9.0% to 10.4% in the over 80 age group. However, data from the NorPD show that in 2011 only 2.4% of total DDDs (Defined Daily Doses) [10] prescribed of blood glucose-lowering drugs were dispensed to institutions. For insulins, 3.4% of total DDDs prescribed were dispensed to institutions. This is probably too little to have any major influence on our observed time trends.

Migration of patients has not been taken into account in the study. In principle, a prevalent user of blood glucose-lowering drugs, moving from another country to Norway, may therefore have been mistakenly counted as an incident user. Again, this is likely to be a minor source of error, particularly for time trends.

The use of dispensed drugs in a prescription database as a proxy for diabetes will obviously result in an underestimation of the total number of patients with diabetes in the population, as a considerable proportion of Type 2 diabetes patients are treated with lifestyle measures alone. Studies from Norway in the period 1995-2005 report that 20-35% of prevalent patients are treated by diet only [13][14][15]. Data from 33 general practices in two representative areas of Norway showed a decline in the proportion of patients with diabetes not treated with blood glucose-lowering drugs, from 30.7% in 1995 to 28.4% in 2005 [14]. Data from the Swedish Diabetes Register show a decline in the proportion of patients with Type 2 diabetes treated with diet only from 25.3% in 2008 to 23.4% in 2011 [16]. We do not have nationally representative data on the proportion of diabetes patients not treated with blood glucose-lowering medication for our study period (2005-2011) but if there is a real decline in this proportion, it is particularly interesting that the incident use of blood glucose-lowering drugs did not increase during the study period. The proportion of patients not treated may also be influenced by the number of new blood glucose-lowering drugs available on the market promoted by the pharmaceutical industry.

On the other hand, we cannot rule out the possibility that the proportion of diagnosed Type 2 diabetes patients treated with OAD has declined during the study period; for instance, as a result of patients being diagnosed earlier and increasing focus on beneficial dietary changes. Safety concerns in the elderly regarding polypharmacy and hypoglycaemic episodes could also contribute to such a trend. The large majority of patients receiving OAD are likely to have Type 2 diabetes. On the other hand, there will be some misclassification from prescriptions for patients with PCOS, pre-diabetes and the metabolic syndrome [17][18][19]. Some women of reproductive age who are treated with metformin are probably suffering from PCOS rather than diabetes. In 2011, the total number of women in this age group treated with metformin was 3,482 (0.54% in this age group population), and a total of 3,712 (0.58%) received oral blood glucose-lowering drugs. In European studies, the occurrence of PCOS has been reported to be 6.5-8% [20].

Since insulins and analogues are used by both patients with Type 1 diabetes and Type 2 diabetes, the definition applied in the outcome measure "prevalent user of insulins only" is aimed at estimating a group of patients with Type 1 diabetes. Some of the defined insulins only users may have received treatment with OAD prior to the 24 month period, as suggested in our sensitivity analysis that extends the period to six years. This could be explained by switching from OAD to insulins only e.g. due to the relative contraindication of OAD in severe heart and kidney failure. Thus, the estimated prevalence of Type 1 diabetes in the population in this study may be overestimated to some extent in the elderly.

There was a stable incidence of use of insulins only. Recent data on childhood-onset Type 1 diabetes from the Norwegian Childhood Diabetes Registry show little or no increase in the past few years, which is consistent with the current results [6]. In the only previous Norwegian study of Type 1 diabetes incidence in individuals over 15 years of age from Norway, Joner & Søvik estimated an incidence of 17/100,000 person years in the age group 15-29 years during 1978-1982 [21]. The current study showed 32/100,000 person years across all age groups. In the age groups over 30-40 years, diabetes classification becomes more challenging. It is nevertheless clear that a substantial proportion of incident Type 1 diabetes cases arise in adulthood [22].

Conclusions

There was no increase in the incident use of blood glucoselowering drugs in Norway in the period 2006-2011 and there was even a decline in the incident use of OAD, particularly in those aged 70 years and older. This occurred despite an increase in prevalent use of OAD. The prevalence and incidence of insulin use only was stable during the study period. This may indicate that the increase in diabetes occurrence in Norway is levelling off, at least temporarily.

Figure 1
1Flow chart illustrating the definitions used for classifying subjects as (a) prevalent and (b) incident users of any blood glucose-lowering drug, OAD and insulins only. Figures for 2010 are shown.

Figure 2
2Prevalent use of blood glucose-lowering drugs by age and gender in Norway in 2010. Solid lines: any blood glucose-lowering drug (A10). Dashed lines, oral antidiabetic drugs. Short dashed lines: insulins only; i.e. individuals having insulins dispensed in 2010, but no OAD in the previous 24 months or in the subsequent 12 months after the first prescription filled.

Figure 3
3Time trends in prevalence of OAD (A10B) use by 10 year age groups in (a) men and (b) women.

Figure 4
4Incident use of blood glucose-lowering drugs by age and gender in Norway. Incident use is defined as no use of blood glucose-lowering drugs in the previous 24 months.(a) OAD (A10B); period 2006-2011. (b) Insulins and analogues (A10A); period 2006-2010. No OAD dispensed in the subsequent 12 months.

Figure 5
5Time trends in incident use of blood glucose-lowering drugs in Norway. Incident defined as no use of blood glucose-lowering drugs in the previous 24 months. In addition for incident use of insulins only, no OAD dispensed in the subsequent 12 months. (a) Incident use of OAD (A10B) in men (b) Incident use of OAD in women. (c) Incident use of insulins only (A10A) in men (d) Incident use of insulins only in women.

Table 1 )
1. The decreasing incidence of OAD use during the study period was largely confined to the 70 years or older age group (IRR per year = 0.97

Table 1
1Incident users of any blood glucose-lowering drug, oral antidiabetic drugs (OAD) and insulins only*Any blood glucose-lowering drug (A10)
OAD (A10B)
Insulins only (A10A only)

Year
Person
years

No of
patients

Incidence rate
per 100,000
person years

95%
confidence
interval

No of
patients

Incidence rate
per 100,000
person years

95%
confidence
interval

No of
patients

Incidence rate
per 100,000
person years

95%
confidence
interval

Women

2006
2,347,036
7,531
320
(314, 328)
6,752
288
(281, 295)
670
29
(26, 31)

2007
2,366,461
7,769
328
(321, 336)
6,927
293
(286, 300)
679
29
(27, 31)

2008
2,390,716
7,677
321
(314, 328)
6,717
281
(274, 288)
783
33
(31, 35)

2009
2,418,595
7,832
323
(317, 331)
6,912
286
(279, 293)
745
31
(29, 33)

2010
2,445,249
7,778
318
(311, 325)
6,817
279
(272, 286)
784
32
(30, 34)

2011
2,473,228
7,269
293
(287, 301)
6,369
258
(251, 264)
 †

Men

2006
2,314,006
7,829
338
(331, 346)
6,830
295
(288, 302)
756
33
(30, 35)

2007
2,342,823
8,374
357
(350, 365)
7,326
313
(306, 320)
793
34
(32, 36)

2008
2,377,361
8,655
364
(356, 372)
7,498
315
(308, 323)
845
36
(33, 38)

2009
2,411,205
8,620
357
(350, 365)
7,453
309
(302, 316)
839
35
(33, 37)

2010
2,443,697
8,605
352
(345, 360)
7,475
306
(299, 313)
813
33
(31, 36)

2011
2,479,989
8,246
332
(325, 340)
7,131
288
(281, 294)
 †

Total

2006
4,661,041
15,360
329
(324, 334)
13,582
291
(287, 297)
1,426
31
(29, 32)

2007
4,709,284
16,143
342
(338, 348)
14,253
303
(298, 308)
1,472
31
(30, 33)

2008
4,768,077
16,332
342
(337, 348)
14,215
298
(293, 303)
1,628
34
(33, 36)

2009
4,829,800
16,452
340
(335, 346)
14,365
297
(293, 302)
1,584
33
(31, 34)

2010
4,888,946
16,383
335
(330, 340)
14,292
292
(288, 297)
1,597
33
(31, 34)

2011
4,953,217
15,515
313
(308, 318)
13,500
273
(268, 277)
 †

*

Incident use is defined as no use of blood glucose-lowering drugs in the previous 24 months. In addition, for insulins only no OAD was dispensed in the
subsequent 12 months.

 †

For insulins only users there are no data for 2011 due to the definition applied, requiring a 12 month follow up period after first insulin prescription filled.

http://www.biomedcentral.com/1471-2458/14/520
AcknowledgmentsA working group was established by the Norwegian Diabetes Association to conduct this study. As well as the authors, the group consisted of: Geir Joner, John Cooper and Tor Claudi. We would also like to thank Liv Nordby and Bjørnar Allgot from the Norwegian Diabetes Association for co-ordination and support.Additional filesAdditional file 1:Table S1. Effect of extending the period for: * no previous use of oral antidiabetic drugs (A10B) on estimated number of prevalent users in 2010; ** no previous use of blood glucose-lowering drugs (A10) on estimated number of incident users in 2010.Table S2. Prevalent users of blood glucose-lowering drugs (A10) in Norway 2005-2011.Table S3. Prevalent users of oral antidiabetic drugs (A10B) in Norway 2005-2011.Table S4. Incident users and incidence rate (per 100,000 person years) of use of oral antidiabetic drugs (A10B) in Norway 2006-2011 (no A10 previous 24 months).Table S5. Incident users and incidence rate (per 100,000 person years) of insulin only use 2006-2010 in Norway (no A10 previous 24 months and no A10B 12 months after first insulin prescription dispensed).Table S6. Mean population used in calculation of incidence rates and prevalences of use of blood glucose-lowering drugs in Norway 2005-2011.Additional file 2:Figure S1. New users of insulins only among women aged 30-39 years in 2008.Abbreviations ATC: Anatomical therapeutic chemical; DDD: Defined daily dose; NorPD: Norwegian prescription database; OAD: Oral antidiabetic drugs; PCOS: Polycystic ovary syndrome.Competing interestsThe authors declare that they have no competing interests.Authors' contributions HS conceived the study and drafted and edited the manuscript. KIB, HSc, TJB, AKJ, KM, CB and LCS developed the design, interpreted the results and revised the manuscript. RS participated in the design of the study and performed the statistical analysis. All authors reviewed the manuscript for intellectual content and approved the final manuscript.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Strøm, No increase in new users of blood glucose-lowering drugs in Norway. 14520: a nationwide prescription database study. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Strøm et al.: No increase in new users of blood glucose-lowering drugs in Norway 2006-2011: a nationwide prescription database study. BMC Public Health 2014 14:520. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit


Assessing clustering of metabolic syndrome components available at primary care for Bantu Africans using factor analysis in the general population

John Nasila Sungwacha
Joanne Tyler
Benjamin Longo-Mbenza
Jean Bosco
Kasiam Lasi On'kin
Thierry Gombet
Rajiv T Erasmus
Assessing clustering of metabolic syndrome components available at primary care for Bantu Africans using factor analysis in the general population
R E S E A R C H A R T I C L E Open Access
Background: To provide a step-by-step description of the application of factor analysis and interpretation of the results based on anthropometric parameters(body mass index or BMI and waist circumferenceor WC), blood pressure(BP), lipid-lipoprotein(triglycerides and HDL-C) and glucose among Bantu Africans with different numbers and cutoffs of components of metabolic syndrome(MS). Methods: This study was a cross-sectional, comparative, and correlational survey conducted between January and April 2005, in Kinshasa Hinterland, DRC. The clustering of cardiovascular risk factors was defined in all, MS group according to IDF(WC, BP, triglycerides, HDL-C, glucose), absence and presence of cardiometabolic risk(CDM) group (BMI,WC, BP, fasting glucose, and post-load glucose).Results: Out of 977 participants, 17.4%( n = 170), 11%( n = 107), and 7.7%(n = 75) had type 2 diabetes mellitus(T2DM), MS, and CDM, respectively. Gender did not influence on all variables. Except BMI, levels of the rest variables were significantly higher in presence of T2DM than non-diabetics. There was a negative correlation between glucose types and BP in absence of CDM. In factor analysis for all, BP(factor 1) and triglycerides-HDL(factor 2) explained 55.4% of the total variance. In factor analysis for MS group, triglycerides-HDL-C(factor 1), BP(factor 2), and abdominal obesity-dysglycemia(factor 3) explained 75.1% of the total variance. In absence of CDM, glucose (factor 1) and obesity(factor 2) explained 48.1% of the total variance. In presence of CDM, 3 factors (factor 1 = glucose, factor 2 = BP, and factor 3 = obesity) explained 73.4% of the total variance. Conclusion: The MS pathogenesis may be more glucose-centered than abdominal obesity-centered in not considering lipid-lipoprotein , while BP and triglycerides-HDL-C could be the most strong predictors of MS in the general population. It should be specifically defined by ethnic cut-offs of waist circumference among Bantu Africans.

Background

Metabolic syndrome (MS) is defined by a cluster of cardiovascular risk factors such as obesity (abdominal obesity in particular), diabetes mellitus (DM), high blood pressure (BP)/hypertension, dyslipidemia, insulin resistance, and hypercoagulability [1][2][3][4][5]. Furthermore, MS is nowadays a major public health problem worldwide [6]. Before the advent of the consensus of the definition of the MS using International Diabetes Federation(IDF) [6], Experts from WHO and EGIR required the measurement of serum insulin levels, diabetes mellitus(DM) or glucose intolerance, hypertension, triglycerides, HDL-Cholesterol (C), body mass index(BMI), and waist circumference(WC) with different cutoff points [7,8]. However, MS characterizes by international cutoff points [6][7][8][9], are limited to detect efficiently bantu Africans at higher cardiometabolic (CDM) risk(hypertension, DM, atherosclerosis) in comparisons with ethnic specific definition of MS [10]. Africans have low or normal lipid profile [11,12] and a high level of HDL-C [13]. Longo-Mbenza et al. include low birth weight, coronary heart disease, malnutrition, elevated fibrinogen, total cholesterol, and urea nitrogen [11][12][13].

In sub-Saharan Africa, MS, obesity, dyslipidemia, DM, hypertension and DM are emerging with cardiovascular complications [14][15][16][17][18][19][20][21] because of urbanization, migration, epidemiologic transition, demographic transition, and nutrition transition [22][23][24]. Identifying patterns of Bantu Africans at the primary care level can explain, at least in part, the differences observed in the prevalence or incidence of MS and cardiovascular diseases between different populations [25][26][27][28][29][30].

Several statistical methods can be used to identify patterns of clustering in cardiovascular diseases such as DM and hypertension. One such important and useful technique is factor analysisa multivariate technique [31][32][33][34][35][36]. Indeed, Factor analysis is a statistical method used to describe variability among observed variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis searches for such joint variations in response to unobserved latent variables. The observed variables are modelled as linear combinations of the potential factors, plus "error" terms. The information gained about the interdependencies between observed variables can be used later to reduce the set of variables in a dataset. Furthermore, at our knowledge, there is no information on the physiogenic process including the mechanisms with which the major components of the MS relate to each other which could be one of the features to make preventive startegies and control of emerging cardiovascular diseases in Africa [17][18][19][20]. For that reason, the objective of this study was to provide a step-by-step description of the application of factor analysis and interpretations of the results based on the clustering of anthropometric parameters, blood pressure, triglycerides, HDL-C, and plasma glucose in all, presence of MS defined by IDF, absence and presence of CDM(exclusion of triglycerides and HDL-C).

Methods

This study was a cross-sectional survey conducted between January, and April 2005, in Kinshasa Hinterland with details previously published [13]. This study was carried out in compliance with the Helsinki Declaration (59 th WMA General Assembly, Seoul, South Korea, October 2008. http://www.wma.net/en/30publications/ 10policies/b3/index.html). This research was approved by the Ethics Committee of Lomo Medical Clinic (Ref-00038-03-07) at Kinshasa Limete. Fully informed and written consent was obtained from all adult participants.

The survey was specifically and extensively designed using a statistical multistage and stratified random model at each level to recruit a study sample with similar and representative characteristics of Kinshasa Hinterland demographic and socioeconomic structure and results comparable with global data on DM.

Each region contributed with a number of cluster (EDs) calculated by population number: 185, 112 inhabitants for the upper urban area of Gombe, 161,410 inhabitants of the semi-rural Kisero area, 153,265 inhabitants for the urban Lukemi area and 146,034 inhabitants for the deepest rural Feshi area. The sample size was calculated as Z 2 xPxQx the expected prevalence of DM in each area, Q = 1-P, d is the in the absolute accuracy of 2% ad f = 8.5 to correct the design effect.

The details of collection of weight, height, waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), plasma fasting glucose and plasma post load glucose have been described elsewhere [30][31][32].

Definitions

Body mass index (BMI) was obtained in dividing weigh (kg) by height (m) 2 . In our setting with limited resources and lack of routinely measured insulin resistance (gold standard), we applied the criteria of MS diagnosis proposed by the International Diabetes Federation (IDF) as follows: raised systolic blood pressure (SBP > 130 mmHg) and diastolic blood pressure (DBP > 85 mmHg), elevated triglycerides (TG > 1.7 mmol/L), low high-density lipoprotein cholesterol (HDL < 1.04 mmol/L in men and <1.29 mmol/L in women) levels, abdominal obesity defined by increased waist circumference (WC > 94 cm in men and >80 cm in women), and fasting plasma glucose (FPG > 5.6 mmol/L) (6).

CDM was defined by the constellation of 3 components of WHOdefined MS such as diabetes, hypertension, and BMI > =30 kg/m2. However, absence of CDM was defined in participants without pre-hypertension, abdominal obesity, BMI > =25 kg/m2, and CDM. The definition of diabetes was based on clinical arguments and the latest WHO/IDF criteria among persons with the fasting venous plasma glucose level > =126 mg/dL or Post-load venous blood plasma level > =200 mg/dL [7]. This was an undiagnosed T2DM so that information about HbA1c, duration of diabetes, and medications was not available and compulsory.

Statistical analysis

Data were presented as mean ± SD. Factor analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data.

Factor analysis is based on the following statistical model and definitions

Suppose we have a set of p observable random variables, x 1 ,…,x p with means. μ 1 ,…,μ p .

Suppose for some unknown constants l ij and k unobserved random variables F j , where i ϵ 1,…, p and j ϵ 1,…, where k < p, we have
x i À i ¼ l i1 F 1 þ ⋅⋅⋅ þ l ik F k þ ε i :
Here, the ε i 's are independently distributed error terms with zero mean and finite variance, which may not be the same for all i. Let Var(ε i )ψ i , so that we have Cov ε ð Þ ¼ Diag ψ 1 ; …; ψ ð Þ¼Ψ and E ε ð Þ ¼ 0 :

In matrix terms, we have
x− ¼ LF þ ε :
If we have n observations, then we will have the dimensions x p×n , L p×k , and F k×n . Each column of x and F denote values for one particular observation, and matrix L does not vary across observations. Also we will impose the following assumptions on F.
1. F and ε are independent. 2. E(F)=0 3. Cov(F)=I
Any solution of the above set of equations following the constraints for F is defined as the factors, and L as the loading matrix.

Suppose Cov(x − μ) = Σ. Then note that from the conditions just imposed on F, we have
Cov x− ð Þ ¼ Cov LF þ ε ð Þ; or ∑ ¼ LCov F ð ÞL T þ Cov ε ð Þ; or ∑ ¼ LL T þ Ψ:
Note that for any Orthogonal Matrix Q if we set L=LQ and F=Q T F, the criteria for being factors and factor loadings still hold. Hence a set of factors and factor loadings is identical only up to orthogonal transformations.

Common factor analysis, also called principal factor analysis (PFA) or principal axis factoring (PAF), seeks the least number of factors which can account for the common variance (correlation) of a set of variables.

Analogous to Pearson's r, the squared factor loading is the percent of variance in that indicator variable explained by the factor. To get the percent of variance in all the variables accounted for by each factor, the sum of the squared factor loadings for that factor (column) was added and divided by the number of variables. This is the same as dividing the factor's Eigenvalue by the number of variables.

The Eigenvalue for a given factor measured the variance in all the variables which is accounted for by that factor. Eigenvalues measure the amount of variation in the total sample accounted for by each factor.

Extraction sums of squared loadings were performed. Factor scores were the scores of each case (row) on each factor (column). To compute the factor score for a given case for a given factor, the case's standardized score was taken on each variable, multiplied by the corresponding factor loading of the variable for the given factor; and these products were summed.

For determining the number of factors, the Kaiser criterion was used. The Kaiser rule is to drop all components with Eigenvalues under 1.0.

The Cattell scree test plotted the components as the X axis and the corresponding Eigenvalues as the Y-axis. As one moves to the right, toward later components, the Eigenvalues drop. When the drop ceases and the curve makes an elbow toward less steep decline, Cattell's scree test says to drop all further components after the one starting the elbow.

Varimax Rotation served to make the output more understandable and facilitated the interpretation of factors. This is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor matrix, which has the effect of differentiating the original variables by extracted factor. This procedure yields results which make it as easy as possible to identify each variable with a single factor. To avoid theoretical supposed grounds, we used oblique Promax rotation as additional alternative to varimax rotation for suited clustering characteristics.

A P-value < 0.05 was considered as statistically significant. All analyses were performed using the Statistical Package for Social Sciences (SPSS) for windows version 18.0 (SPSS Inc) Chicago, Il, USA.

Results

Out of the original population (n = 977 with 458 males and 519 females), 170(17.4%), 107(11%), and 75(7.7%) were diagnosed for new T2DM, MS, and CDM, respectively. Table 1 describes the mean levels of general characteristics according to T2DM status. Except similar(P > 0.05) values of BMI in presence and absence of T2DM, levels of age, WC, SBP, DBP, and triglycerides were significantly (P < 0.05) higher in T2DM participants than no diabetic participants. However, HDL-C values were significantly (P < 0.05) lower in T2DM presence than diabetes absence. The mean levels of age, BMI, WC, SBP, DBP, triglycerides, HDL-C, FPG,and post-load glucose in men were similar (P > 0.05) with those from women(results not shown).

In the general population, factor analysis generated 2 factors which were explaining 55.4% of total variance: factor 1 (Blood pressure with variance = 29.6%; DBP = 0.881 and SBP = 0.872), and factor 2(Dylipidemia with variance = 25.8%; HDL-C = -0.886 and triglycerides = 0.872).

In MS participants, factor analysis generated 3 factors with total variance of 75.1%: factor 1(Dyslipidemia with variance = 29.5%; triglycerides = 0.911 and HDL-C = -0.874), factor 2(Blood Pressure with variance = 27.9%; DBP = 0.869 and SBP = 0.837), and factor 3 (Abdominal obesity + Dysglycemia with variance = 18.1%; WC = 0.836 and fasting plasma glucose = 0.609).

Absence of CDM(n = 572) Table 2 describes the mean values of variables analyzed in participants without CDM. The correlation matrix in absence of CDM is presented in Tables 3 and 4. Postload plasma glucose was significantly and positively correlated to BMI and WC, but significantly and negatively correlated to both SBP and DBP. SBP was significantly and positively correlated to BMI but significantly but negatively correlated to FPG. DBP was significantly and negatively correlated to FPG.

Factor analysis revealed two uncorrelated factors that cumulatively explained 48.1% of the observed variance of the absence of CDM. The number of those two factors was determined by the scree plot according to Eigen-value ( Figure 1). These two factors could be identified as Blood Glucose Metabolism Disordering (Factor 1; 26.3% of variance) and obesity (Factor 2; 22% of variance) ( Table 5 and Figure 2).

Presence of CDM

The mean values of variables analyzed in participants with CDM are presented in Table 6. Factor analysis revealed three uncorrelated factors that cumulatively explained 73.6% of the observed variance in the presence of cardiometabolic risk. The number of these three factors (Components) was determined by the scree plot according to Eigen-values ( Figure 3).

Discussion

The present study identified MS combination for which factor analysis would be appropriate among Bantu Africans. For that reason, the steps involved in performing factor analysis procedure were described. Thus, factor analysis findings using SPSS software have been interpreted. However, MS is a complex issue in health care. It does not have a simple cause, but multiple risk factors. Its natural course is influenced by genetic factors, personal (Host) attributes, environmental characteristics, or some interactions of both.

At our knowledge, this was the first study to characterize factor analysis of possible risks for clustering of some traditional cardiovascular risk factors in the general population, absence of CDM, presence of CDM, and presence of MS among Bantu Africans living in DR Congo(Central region).

The extent of T2DM, CDM (concurrent presence of 3 non-lipid components of MS), and MS defined by IDF 5 criteria such as 3 non-lipid components and 2 lipidlipoprotein components [6] was examined. The present study also determined the interrelation of the main CDM factors: BMI, WC, SBP, DBP, FPG, and post-load glucose.

Emerging burden of MS

Contrary to the previous myths, non communicable diseases (Diabetes, hypertension, MS, atherosclerosis) are no longer rare in Africa [10][11][12][13][14]. The extent is increasing and it is thought to be due to the shifting from traditional African customs to the Western lifestyle [15][16][17][18].

MS pattern

The present study sought at identifying the physiogenic factors responsible for the clustering of cardiometabolic components. Factor analysis showed marked differences in the MS pattern between the groups of 3 components (CDM) and 5 components (MS).

Number of generated factors

In the general adult population, factor analysis identified 3 components for MS. This finding about MS was consistent with a study conducted in Asian Indians from the general population [1]. In India, however, the total variance of 65.3% [1] was lower than the total variance of 75.1% explained in the present study. However, in the South African general population, 5 factors could be identified in factor 1(Obesity), factor 2(Hypertension), factor 3(Hyperuricemia-hypertriglyceridemia), factor 4 (Hyperglycemia), and factor 5(Hyperinsulinemia) [1]. In our findings, the first 2 factors cumulatively explained 58% of the total variance for MS. Only considering 3 non-lipid components, affordable in limited resources areas, factor analysis had identified also 3 factors with total variance almost 74.5% for CDM and similar with that for MS. The first 2 factors(Dysglycemia and Hypertension) cumulatively explained 56% of the total variance of CDM.

In considering the entire population and the subpopulation without CDM, factor analysis generated only 2 factors. In all participants, the factors revealed such as hypertension(factor 1) and dyslipidemia(factor 2) cumulatively explainedb55.4%bof the total variance of the clustering pattern of atherogenic factors from MS. However, in the absence of CDM, BP was not loaded, while only dysglycemia(factor 1) and obesity/BMI and WC(factor 2) were revealed the first factors which cumulatively explained 48.1% of the total variance of the characterization of this group by the clustering of non-lipid components for MS.

The present study showed that no overlapping of variables on more than 1 factor indicated that more than 1 variable was responsible for the ultimate phenotype of the MS. Our findings demonstrated that factor analysis confirmed the general results from other factor analyses of the MS on different ethnic groups that had 3-5 factors revealed [1][2][3][4].

Our findings with the clustering of the variables in MS as a result of multiple factors known modifiable in nature raised the following question: would it be more efficient to include all participants in one major factor analysis model? Indeed, factor analysis is practically limited to develop a single-parameter screening tool for MS in this study as mentioned in the literature [4]. IDF recommended WC as the most frequently used anthropometric index to define abdominal obesity [6]. Paradoxically, WC, BP, lipid, and glucose levels were similar among men and women in this study as reported in the same general population [10]. However, WC cutoff points differ by ethnic groups and gender worldwide [1,4]. Older age was associated with T2DM in this study, while age not considered as a component of MS, is a confounding factor for anthropometric variables of MS amon Taiwanese individuals [4].

Factor analysis was applied to see whether there was a less complex space with fewer than the "n" dimensions of the variables that had been analyzed. It was found that a three dimensional space or a mixture of three factors could be used to explain a major part of the data. In  more precise mathematical terms the global and examined variables without dyslipidemia(with paradoxes of triglycerides and HDL-C) could be reduced to three factors with eigenvalues greater than one, which explained 73.4% of the variance in MS Africans. The loadings on these factors sorted out into three metabolic groupings. Neither of the variables was loaded on all the three components. These three factors could be identified as Glucose Metabolism (Factor 1), Blood Pressure (Factor 2) and Obesity (Factor 3). This suggests that those nonlipid components clustered naturally rather than as a result of chance.

No overlapping of variables on more than one factor indicated that more than 1 variable is responsible for the ultimate phenotype of the fats. The present factor analysis confirmed global results from other factor analyses of fats among different populations that had 3 to 4 factors identified as non-modifiable/genetic risk factors and modifiable/ environmental risk factors. The study attempted to observe among BMI, WC, SBP, DBP, FPG, and post-load PG group -which ones go together and which ones do not [30]. Variables with a factor loading of at least 0.3 have generally been considered for interpretation although it is suggested that only loadings ≥ 0.4 be used, which therefore shares at least 15% of the variance with a factor, should be used in the study [24].

In many studies, fats play a pivotal role in the occurrence of the onset of CVD, andT2DM. However, lipid profile and fasting insulin are not available in the majority of health centers in developing countries.

Therefore, identification of non-lipid components of the metabolic syndrome would be helpful in understanding the etiology among Bantu Africans. Virtually no study has been performed on combination of the evaluated variables in Sub-Saharan Africa.

Perspectives for Africa

This study highlighted the absence of obesity as a factor of MS in type 2 diabetic Bantu Africans. Moreover, obesity was the third factor of MS with lower variance in comparisons with variances of factor 1(Glucose) and factor 2(Blood pressure) among type 2 diabetic Africans with MS. As reported on the factor analysis of risk variables associated with MS in adult Asian Indians [1], further studies among larger sizes from Bantu Africans, are needed to demonstrate the responsibility of more than one underlying physiogenetic polymorphisms in the present specific glucose-centered pattern for MS with lower BMI and smaller WC.

Limitations and strengths

The advantages and disadvantages of factor analysis have been reported in medical, physical, marketing economic and environmental researches [31]. There are different  reasons of the limitations of this study, that is, ethnic and cultural heterogeinity, genetic studies, gender, age composition, number of risk variables included, sample size, and cutoff points of MS and CMD[ ]. In Asian Indians, angiotensin converting enzyme gene polymorphism(insertion/deletion) with BP was identified factor 3 along lipids and lipoproteins(factor 1) and centripetal fat and BP(factor 3) associated with MS phenotype [1]. In these Asian Indians, DBP in factor 2 overlapped on another variable in factor 3 [1].

Advantages of factor analysis

The rotation methods are useful in making the output more understandable and for ease of interpretation of the factors. The optimal variance of the squared loadings of a factor (Column) on all the variables (rows) in a factor matrix is due to varimax rotation (an orthogonal rotation of the factor axes). Factor matrix differentiates the original variables from extracted factors. Groups of inter-related variables are identified and seen in their manner to be related to each other.

In multi-factorial diseases, it is easy and inexpensive to perform factor analysis which can be used to identify hidden dimensions which may not be apparent from analysis.

Disadvantages of factor analysis

It is not possible to pick the proper rotation using factor analysis alone as all rotations represent different underlying processes and equally valid outcomes of standard factor analysis optimization.

Though not a strictly mathematical criterion, there is much to be said for limiting the number of factors to those whose dimension of meaning is readily comprehensible. The same limitation is reported about variance explained criteria.

The research is requested to choose the solution which generates the most comprehensive evaluation of data.

The Kraiser criterion is the default in SPSS and most computer programs but is not recommended when used as the sole cut-off criterion for estimating the number of factors.

Certain researchers prefer to keep enough factors to account for 80%-90% of the variation. However, other researchers explain variance with a few factors, but lower than 50% (Parsimony).

Factor analysis cannot identify causality as interpreting factor analysis is based on using a " heuristic" convenient solution even if not absolutely "true". If important attributes (such as lipid components of fats) at primary health care in developing countries like DRC, the value of the procedure was reduced for BMI in absence of MS.

It requires strong background knowledge of biology and Pathophysiology or theory as multiple attributes may be highly correlated for no apparent reason. Varimax was an orthogonal rotation of the components to maximize the variance of the squared loadings (unrotated output accounted for by the first and subsequent factors) of a dimension (Column) on all the variables(Rows) in a factor matrix. Varimax rotation is the easiest and the most simple and common rotation option used in MS [1][2][3][4][5]. However, oblique rotations might be more suited and more preferred with methods inclusive [31]. In search of underlying dimensions, the use (sometimes an abuse) of factor analysis in Personnality and Social Psychology literature [32]. There are also different rotation methods such as quartimax rotation(an orthogonal alternative), equimax rotation( a compromise between varimax and quartimax criteria), direct oblimin rotation(standard method with a non-orthogonal/oblique rotation with higher eigenvalues but lower interpretability of the factors), and Promax rotation. In this study, we evaluated Promax rotation in addition to varimax rotation. Indeed, Promax rotation was computationally faster alternative nonorthogonal/oblique rotation method than other oblique methods such as direct oblimin rotation. The potential limitations such as the inability of the investigators in collecting sufficient set of product activities, unknown on reasons of associated dissimilar attributes, and obscured factors were excluded or minimized.

Implementation of factor analysis

The implementation of Factor analysis is well established within robust statistical software such as SAS, BMDP and SPSS and R programming language with the factanal function (GPA rotations), and Open Opt [33]. This is evidenced by both analysis and scree plots and the three dimensional charts.

Conclusion

The factor analysis performed for this study suggests that the clustering of the non-lipid variables is sufficient to define CDM in black Africans at including glucose metabolism, Blood pressure and Obesity. Since 3 factors in sequencing dyslipidemia, hypertension, and abdominal obesity-dysglycemia were identified for the Bantu Central African MS phenotype, more one major factor could be accounted for this specific MS. Early prevention and

Figure 2
2Two-component plot in rotated space among participants without cardiometabolic risk.

Figure 3
3Eigen values among participants with cardio-metabolic risk.

Table 1
1Association of age and metabolic syndrome components with incident type 2 diabetes mellitus in the study populationVariables of
interest

Participants with
incident types 2 DM

Absence of diabetic
participants

P Values

Age (Years)
53 ± 13
36 ± 12
< 0.0001

BMI (Kg/m 2 )
24.4 ± 5.6
23.3 ± 5.4
0.881

WC (cm)
86 ± 14.7
78.7 ± 14.3
< 0.0001

SBP (mm Hg)
131.1 ± 31.1
117 ± 17
< 0.0001

DBP (mm Hg)
80 ± 16.7
70 ± 11.7
< 0.0001

FPG (mmd/L)
7.5 ± 2
5.1 ± 1
< 0.0001

Triglycerides
(mmd/L)

3.8 ± 0.8
2.7 ± 0.9
< 0.0001

HDL -C
(mmd/L)

1 ± 0.3
2 ± 0.6
< 0.0001

Retrieved from "http://en.wikipedia.org/wiki/Factor_analusis."

Table 2
2Characteristics in absence of cardiometabolic riskVariables
Mean±SD

BMI (Kg/m 2 )
24.6 ± 8.80

Waist Circumference (CM)
79.8 ± 13.6

SBP (mmHg)
113.4 ± 12.4

DBP (mmHg)
66.6 ± 07.6

FPG (mg/dL)
82.0 ± 14.0

Post-Load PG (mg/dL)
123.21 ± 18.0

Table 3
3Correlation matrix in absence of cardiometabolic riskWC
FPG
Post-load PG

BMI
-
-
0.132

P = 0.016

SBP
-
-
−0.114

P = 0.031

DBP
-
-
−0.125

P = 0.020

WC
0.035
0.146

P = 0.286
P = 0.008

Table 4
4Correlation matrix in absence of cardiometabolic riskBMI
SBP
DBP

BMI
-
0.104
0.052

P = 0.046
P = 0.197

SBP
-
-
0.128

P = 0.019

DBP
-
-
-

WC
0.200
0.063
−0.021

P < 0.0001
P = 0.286
P = 0.364

FPG
−0.086
−0.155
−0.118

P = 0.080
P = 0.006
P = 0.027

Table 5
5Rotated component matrix in absence of cardiometabolic riskFactor 1
Factor 2

BMI
−0.131
0.738

SBP
−0.502
0.340

DBP
−0.455
0.114

WC
0.077
0.696

FPG
0.765
0.051

Post-Load PG
0.707
0.400

Table 6
6In participants with cardiometabolic risk 2Variables
Mean±SD

BMI (Kg/m 2 )
43.40 ± 20.20

Post-Load PG (mg/dL)
80.00±15.80

SBP (mmHg)
127.60 ± 26.70

DBP (mmHg)
80.00 ± 15.80

FPG (mg/dL)
93.40 ± 19.80

Waist Circumference (CM)
182.84 ± 101.10

Table 7
7Rotated component matrix in presence of cardiometabolic riskFactor 1
Factor 2
Factor 3

BMI
0.077
−0.025
0.760

SBP
−0.064
0.881
−0.137

DBP
−0.224
0.833
0.116

WC
0.025
0.001
0.769

FPG
0.906
−0.106
0.096

Post-Load PG
0.894
−0.179
0.031

http://www.biomedcentral.com/1756-0500/6/228
AcknowledgementsWe thank the medical officers, interns, social workers, and nursing staffs from the University of Bandundu, the participants and the professionals of the laboratory of Lomo Medical for the ultra-structural technical assistance.management (diagnosis and proper intervention) strategies for those modifiable loaded risk variables could reduce the burden of type 2 DM, MS, and emerging cardiovascular disease in Central Africa.Competing interestsAll authors declare that they have no competing interests.Authors' contributionsAll the co-authors have seen and approved the final version of the manuscript and it is not currently under active consideration for publication elsewhere, has not been accepted for publication, nor has it been personally and actively involved in substantive work leading to the report, and will hold themselves jointly and individually responsible for its content. JBKLO was responsible for the field work. JNS performed review literature related to factor analysis. BLM conceived of the study, and participated in the study design. JNS and BLM performed statistical analyses. JTL, JNS, BLM, JBKLO, and GT participated in the coordination of writing of the study. All authors read and approved the final manuscript.
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Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution. Nasila Sungwacha, BMC Research Notes. 6228Assessing clustering of metabolic syndrome components available at primary care for Bantu Africans using factor analysis in the general population. Submit your manuscript at www.biomedcentral.com/submitCite this article as: Nasila Sungwacha et al.: Assessing clustering of metabolic syndrome components available at primary care for Bantu Africans using factor analysis in the general population. BMC Research Notes 2013 6:228. Submit your next manuscript to BioMed Central and take full advantage of: • Convenient online submission • Thorough peer review • No space constraints or color figure charges • Immediate publication on acceptance • Inclusion in PubMed, CAS, Scopus and Google Scholar • Research which is freely available for redistribution Submit your manuscript at www.biomedcentral.com/submit