We aimed to characterize metabolic status by body mass index (BMI) status.
The CRONICAS longitudinal study was performed in an age-and-sex stratified random sample of participants aged 35 years or older in four Peruvian settings: Lima (Peru’s capital, costal urban, highly urbanized), urban and rural Puno (both high-altitude), and Tumbes (costal semirural). Data from the baseline study, conducted in 2010, was used. Individuals were classified by BMI as normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30 kg/m2), and as metabolically healthy (0–1 metabolic abnormality) or metabolically unhealthy (≥2 abnormalities). Abnormalities included individual components of the metabolic syndrome, high-sensitivity C-reactive protein, and insulin resistance.
A total of 3088 (age 55.6±12.6 years, 51.3% females) had all measurements. Of these, 890 (28.8%), 1361 (44.1%) and 837 (27.1%) were normal weight, overweight and obese, respectively. Overall, 19.0% of normal weight in contrast to 54.9% of overweight and 77.7% of obese individuals had ≥3 risk factors (p<0.001). Among normal weight individuals, 43.1% were metabolically unhealthy, and age ≥65 years, female, and highest socioeconomic groups were more likely to have this pattern. In contrast, only 16.4% of overweight and 3.9% of obese individuals were metabolically healthy and, compared to Lima, the rural and urban sites in Puno were more likely to have a metabolically healthier profile.
Most Peruvians with overweight and obesity have additional risk factors for cardiovascular disease, as well as a majority of those with a healthy weight. Prevention programs aimed at individuals with a normal BMI, and those who are overweight and obese, are urgently needed, such as screening for elevated fasting cholesterol and glucose.
Citation: Benziger CP, Bernabé-Ortiz A, Gilman RH, Checkley W, Smeeth L, Málaga G, et al. (2015) Metabolic Abnormalities Are Common among South American Hispanics Subjects with Normal Weight or Excess Body Weight: The CRONICAS Cohort Study. PLoS ONE 10(11): e0138968. https://doi.org/10.1371/journal.pone.0138968
Editor: Pedro Tauler, University of the Balearic Islands, SPAIN
Received: May 6, 2015; Accepted: September 8, 2015; Published: November 23, 2015
Copyright: © 2015 Benziger et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: At this time data cannot be made available as it is still under embargo. Although we would like to share it with other researchers, we cannot as we are forbidden by law and a signed contract with National Heart Lung and Blood Institute (NHLBI). We understand PLoS will still be able to publish the paper as in a previous e-mail we received the following information "If there are ethical or legal restrictions preventing you from sharing your data, please describe these for us, and indicate whether an ethically compliant dataset can be made available on request." At this time we are somewhat in the remit of legal restrictions.
Funding: This project and authors ABO, RHG, WC, and JJM have been funded with Federal funds from the United States National Heart, Lung, and Blood Institute, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN268200900033C. CPB was supported by the National Institutes of Health and Fogarty International Center through the International Clinical Research Fellows Program at Vanderbilt University (R24 TW007988). LS is supported by a Wellcome Trust Senior Research Fellowship in Clinical Science. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Obesity, commonly measured using body mass index (BMI), has been associated with a number of metabolic and cardiovascular disease (CVD) risk factors including an excess mortality risk . Levels of overweight and obesity in Latin America have increased over time  and have approached levels found in higher-income countries, with a disproportionate increase in waist circumference compared to BMI over the past 20 years [3, 4]. Overweight and obesity and are projected to continue rising .
The World Health Organization (WHO) recommended cut-off points for overweight and obesity, at BMI values of 25 kg/m2 and 30 kg/m2, respectively, are based on a large number of studies in predominantly Caucasian populations. Prior studies in high-income settings have noted a high prevalence of individuals who are overweight and obese but display normal metabolic features despite their increased adiposity [6–12]. This metabolically healthy obese profile is controversial: whilst some evidence suggests these individuals are still at increased risk of developing diabetes [13, 14], CVD [15, 16] and have increased mortality , other studies have found an “obesity paradox” whereby there seems to be a protective effect of obesity from mortality and other chronic conditions . Fasting insulin levels in obese individuals can help further differentiate healthy versus unhealthy as increased levels are associated with development of risk factors for CVD and increased mortality [11, 18, 19]. Conversely, there are also individuals who are normal weight but display metabolically unhealthy features with increased risk of diabetes and CVD [11, 20, 21]. No prior studies have estimated the prevalence of overweight and obese individuals and their metabolic risk factors and if these individuals are different with regards to socio-demographic and behavioral factors in a Latin American population.
As a rapid nutritional transition occurs in Peru  and the prevalence of obesity continues to increase, it is unknown to what extent these profiles exist in this population and whom to target with public health interventions at the community health worker level. Peru also has geographical diversity; a pattern shared with other Latin American countries, and differences in these locations is unknown. Based on BMI, prior studies in Peru have reported a prevalence of 40% and 15–23% for overweight and obesity, respectively [23, 24], with increased odds of obesity among those who are older and female [24, 25]. The prevalence of metabolic syndrome is between 17–25%, depending on which definition is used (American Heart Association or International Diabetes Federation) [26, 27]. However, none have evaluated the prevalence of metabolically healthy obese or metabolically unhealthy normal weight individuals in different settings in Peru. Therefore, we hypothesized that the metabolically healthy obese group compared to the healthy normal weight group was different with regards to socio-demographic and behavioral factors in a Latin American population. In addition, we hypothesized that the metabolically unhealthy normal weight group compared to the metabolically unhealthy overweight and obese group had different socio-demographic and behavior factors. Given our hypotheses, we aimed to use an established definition of metabolic status and to do the following: 1) estimate the prevalence of each of the BMI categories (normal weight with and without metabolic abnormalities, and overweight and obese with and without metabolic abnormalities); 2) determine the prevalence of metabolically healthy obese according to socio-demographic and behavioral factors; and 3) determine the prevalence of unhealthy metabolic status if normal weight according to socio-demographic and behavioral factors.
Study Design, Setting and Participants
The objectives and design of the CRONICAS cohort study have been reported elsewhere . Briefly, a longitudinal study was performed in an age-and-sex stratified random sample of participants aged 35 years or older in four Peruvian settings: Lima (Peru’s capital, costal urban, highly urbanized), urban and rural Puno (both high-altitude), and Tumbes (costal semirural). Data from the baseline study, conducted in 2010, was used for this study and analysis was completed in 2014.
A team of community health workers was trained to enroll participants and to conduct the questionnaires assessing socio-demographic and behavioral variables (Table 1). Variables included sex, age, study site, education, and socioeconomic status, the latter constructed from the aggregation of assets and household facilities into a wealth index split in tertiles . Behavioral risk factors included were: current daily smoking, hazardous alcohol drinking , and leisure time and transport-related physical activity (Table 1).
Participants were invited to a clinic visit where height, weight, waist circumference, and systolic (SBP) and diastolic (DBP) blood pressure, as well as fasting blood samples were obtained using standardized methods and calibrated tools . Total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-c), insulin, and high-sensitivity C-reactive protein (hs-CRP) were measured in serum, whereas fasting glucose was assessed in plasma.
Body Size Definitions
Subjects were classified based on BMI as normal weight (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30 kg/m2).
Metabolic Status Definition
Metabolic syndrome and its components were defined according to International Diabetes Federation criteria  (Table 1): waist circumference cutoffs (≥90 cm in men or ≥80 cm in women) for South Asian individuals was used as recommended; fasting triglyceride level ≥150 mg/dL; HDL-c level <40 mg/dL in men <50 mg/dL in women; systolic blood pressure ≥130mmHg, diastolic blood pressure ≥85mmHg, antihypertensive medication, or history of hypertension ; fasting glucose level ≥100 mg/dL or glucose-lowering medication. Elevated hs-CRP (>7.11mg/dL, >90th percentile) and homeostasis model assessment of insulin resistance value (HOMA-IR) (>5.00, >90th percentile) were used using cut-offs proposed previously by Wildman et al. . We classified individuals into three risk groups: 0 or 1 abnormal risk factor, 2 abnormal risk factors, and ≥3 abnormal risk factors by BMI. For the regression analysis, individuals were classified as metabolically healthy (0 or 1 abnormal risk factor) or unhealthy (≥2 abnormal risk factors), as previously defined [7, 8].
Initially, a description of the socio-demographic, behavioral and clinical variables overall and according to BMI and metabolic status was performed. Participants that did not have all of the metabolic factors measured were excluded from the analysis. Geometric means were calculated for non-normally distributed continuous variables. Differences in these variables among the three categories (normal weight, overweight, and obese) were analyzed within each metabolic subgroup using Chi-squared test and one-way analysis of variance.
Among normal weight individuals, prevalence ratios of being metabolically unhealthy were calculated using socio-demographic and behavioral variables in unadjusted models. Subsequently, we used multivariable generalized linear models assuming Poisson distribution of the outcome and robust standard errors to obtain prevalence ratios (PR) and 95% confidence intervals (95%CI), adjusting for all the variables simultaneously, without and with waist circumference into the model. Among overweight and obese individuals, prevalence ratios of being metabolically healthy were calculated using socio-demographic and behavioral variables in unadjusted models and subsequently in multivariable generalized linear models as stated previously. STATA 10 (STATA CORP, College Station, Texas, USA) was used for all analyses.
The study was approved by the Institutional Review Boards at Universidad Peruana Cayetano Heredia and A.B. PRISMA, in Lima, Peru, and at the Bloomberg School of Public Health, Johns Hopkins University, in Baltimore, USA. All participants provided verbal informed consent after our research team read the entire informed consent document to them and any questions were answered. Informed consents were verbal because of high illiteracy rates. Both ethics committees approved the verbal consent procedure.
A total of 3088 (85.4%) of 3618 subjects had all the factors measured. The sample had 51.3% females and on average the sample age was 55.6 years (SD ±12.6 years). Based on BMI, 890 subjects (28.8%) were normal weight, 1361 (44.1%) were overweight, and 837 (27.1%) were obese.
Metabolic Risk Profiles
Baseline socio-demographic, behavioral, and clinical variables overall and by BMI categories and metabolic status are shown in Table 2. Each of the five components of metabolic syndrome was prevalent in at least 25% of the population. A total of 2263 (73.3%) had elevated waist circumference, 2048 (66.4%) had low HDL-c, 1329 (43.1%) had elevated triglycerides, 893 (28.9%) had elevated blood pressure, and 798 (25.9%) had impaired fasting glucose.
Fig 1 shows the distribution of number of risk factors by normal weight, overweight and obese separated by site. As BMI increased, the number of risk factors increased. Overall, 19.0% of the normal weight group compared to 54.9% of the overweight and 77.7% of the obese group had ≥3 risk factors (p<0.001), and was consistent at each site.
Fig 1. Prevalence of metabolic status by body mass index (normal weight, overweight and obese) by site.
Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared).
We next determined if there was a subset of the normal weight group who had multiple metabolic risk factors despite their normal BMI. Overall, there were a total of 2324 (75.3%) participants who were metabolically unhealthy and 16.5% were of normal weight. Within the unhealthy group, compared to the overweight and obese individuals, those who were normal weight were more likely to be older, male, live in rural Puno, have less education level, lower wealth index, and lower physical activity (Table 2). Clinically, they also had lower waist circumference, higher blood pressure, higher HDL-c, lower hypertriglyceridemia, lower impaired fasting glucose, and lower HOMA-IR and hs-CRP levels (Table 2).
Alternatively, we determined if there was a subset of the overweight and obese group who were relatively healthy with either none or 1 metabolic risk factor. Overall, there were 762 (24.7%) participants who were metabolically healthy, and of those, 29.3% were overweight and 4.3% were obese (Fig 1). Within the healthy group with none to 1 metabolic risk factor, compared to the normal weight individuals, those who were overweight and obese were more likely to be younger, female, live in Tumbes, have higher education level, and higher wealth index. Clinically, they also had higher waist circumference, lower blood pressure, and higher HDL-c (Table 2).
Prevalence of normal weight and metabolic status
The multivariate model using data from normal weight individuals, the metabolic unhealthy profile was more prevalent in those with older age groups (55–64 years and ≥65 years), females, those living in Tumbes, higher education level, and highest tertile of wealth index (Table 3). After adjusting for waist circumference, the oldest age group, female sex, and the highest tertile of wealth index had a higher prevalence of the unhealthy profiles (Table 3).
Table 3. Unadjusted and multivariable-adjusted prevalence ratios of the metabolic unhealthy profile associated with socio-demographic and behaviors variables among normal weight individuals (N = 889).
Prevalence of overweight/obese and metabolic status
The metabolically healthy obese profile was more prevalent in those individuals living in urban and rural Puno, those with secondary or superior education, hazardous drinkers, and those with high levels of physical activity; whereas, older age groups, male sex, living in Tumbes and waist circumference had a lower prevalence of the metabolically healthy obese profile. In a multivariate adjustment, living in urban and rural Puno remained significant with a higher prevalence of the healthier profile, while older age groups, male sex, living in Tumbes remained significant with a lower prevalence of the healthier profile. In a multivariate adjustment model that included adjusting for waist circumference, only male sex was associated with a lower prevalence of healthy profile; whereas, living in urban and rural Puno had a higher prevalence of the healthier profile (Table 4).
Table 4. Unadjusted and multivariable-adjusted prevalence ratios of metabolic healthy profile associated with socio-demographic and behaviors variables among overweight and obese individuals (N = 2198).
In this study, we report a high prevalence of metabolic abnormalities in Peruvian population. Among normal weight Peruvians, over one-third were metabolically unhealthy, as high or higher than estimates reported in developed countries [7, 34]. There were few overweight and obese individuals with a healthy metabolic profile. Therefore, obesity is highly associated with having additional risk factors for CVD in this population.
Metabolic profiles in the literature
A prior study using the NHANES data found that 30.1% of normal weight Americans were metabolically unhealthy, with a slightly higher percentage (32.8%) in Mexican-Americans . In Canada, one study found that 20% of normal weight people were metabolically abnormal by percentage body fat . Our findings are higher than either of these populations as one-third were metabolically unhealthy (≥2 risk factors) with nearly 20% having ≥3 risk factors. While this is lower than the 54.9% of the overweight and 77.7% of the obese group who had ≥3 risk factors, it is still concerning as Peru was generally considered to be lagging behind many high-income settings in the epidemiological transition from infectious diseases to non-communicable diseases . In addition, there has been a rapid nutritional transition over the past 15 years from undernutrition to over nutrition, which may contribute to the high prevalence of these risk factors . Prevention programs aimed at individuals with a normal BMI, in addition to those who are overweight, are urgently needed, such as screening for elevated fasting cholesterol and glucose.
Prior studies in Peru have noted an increased risk of obesity in females [24, 25], but this is the first study to find that females who are normal weight also have increased risk of being unhealthy in this population. Prior studies in the United States found that older age, males, and those with moderate physical activity were correlated with being normal weight and metabolically unhealthy . Our study also found these factors, as well as living in a high-altitude setting, Puno, had a higher prevalence of the healthier profile. Prior studies have also noted the benefits of high altitude leading to lower weight and CVD rates .
The present analysis expands on previous observations in Caucasian populations that those with overweight and obesity are a heterogeneous group with regards to their metabolic risk factors and not all overweight individuals have increased CVD outcomes and mortality. However, our findings are interesting in that less than 5% of those with obesity have metabolic healthy profiles. Most studies in Caucasian populations have found much higher prevalence of metabolically healthy obese profile: from 7% (Finland) to 28% (United Kingdom) and 31.7% (United States) [7–9, 34]. The reason for this difference is unknown. Correlates of the metabolically healthy obese phenotype in the United States are older age, high physical activity, moderate alcohol use and non-Hispanic black ethnicity . We found a higher prevalence with males and living in urban and rural Puno in this small metabolically healthy obese group. A recent meta-analysis suggests that obesity increased all-cause mortality compared to healthy normal weight individuals and that there is not a protective effect of being obese and healthy , arguing against the “obesity paradox” . In addition, elevated waist circumference has been found to be predictive of mortality at all levels of BMI  as well as predict the development of diabetes [13, 14] and CVD and stroke . The “obesity paradox”, where increased BMI is protective in some chronic conditions and among older adults , is also culturally acceptable and a social norm in Peru, especially among women. Our findings are very important, as better public health messaging is needed to counter this belief. The low prevalence of metabolically healthy overweight and obese individuals is important for both health care providers and policy makers to understand that obesity is not a protective trait and in fact obesity is associated with additional risk factors for CVD.
Regarding the individual components of the metabolic syndrome, all five components had a disturbingly high prevalence—over one-quarter of the population—higher than reported in prior studies in this region [3, 26, 38–40], with the highest prevalence in abdominal obesity (73%) and low HDL-c (66.4%). One study found that exercise programs helped individuals who were metabolically unhealthy and obese become metabolically healthy, yet still obese . Prior studies found the prevalence of overweight and obesity and metabolic syndrome in South America to vary (overweight prevalence 40–69% and obesity prevalence 11–31%) with Brazil being the thinnest and Paraguay having the most obese populations . In that study, the prevalence of obesity in Peru was 16.3%, which is lower than our study where 27.1% are obese. The prevalence of metabolic syndrome in the CARMELA study was between 14–26%, with 18% in Lima . The PREVENCION study in Peru found the prevalence of metabolic syndrome between 17.3%-21.7% in men and 24.0%-25.3% in women depending on which definition was used (American Heart Association or International Diabetes Federation) . Our study, which included HOMA-IR and hs-CRP, found a significantly higher prevalence of metabolic abnormalities with 75.1% having ≥2 abnormal risk factors. When we analyzed the number of metabolic risk factors separately, we found a dose-related response that was consistent across sites and continued when we looked at 4 or more metabolic risk factors (5.5% of normal weight, 25.0% of overweight, and 46.7% of obese individuals had 4 or more metabolic risk factors; p<0.001). One explanation is that there is a strong correlation between BMI and waist circumference in this population and therefore more overweight and obese individuals were likely to have at least one metabolic risk factor: increased waist circumference. However, we found that as BMI increases, the number of additional metabolic risk factors also increases. Elevated HOMA-IR was present predominantly in those who were overweight and obese, consistent with prior studies [6, 18].
Prior studies found more individuals who fell into the metabolically healthy obese category (between 9% to 41%) and found increased mortality in this group, despite varied definitions of metabolic healthy and unhealthy . In our study, the metabolic healthy profile in obese individuals is almost non-existent (less than 5%). Overall, this reflects the epidemiologic transition that is occurring in Peru where non-communicable diseases are becoming more prevalent and the risk factor profile is similar to many developed countries.
This study has some limitations. The definition of metabolic status used in our study was maintained similar to other published studied [8, 10, 11, 21, 32, 42] to facilitate meaningful comparisons. Therefore, our aim was not to assess individual clinical outcomes by BMI status, but the seven risk factors as a group. Therefore, as prior studies have done, we excluded those who were underweight (n = 19), as meaningful comparisons with the other groups, especially with the large number of individuals in the overweight and obese groups, would have been limited by power issues. In addition, waist circumference was one of the metabolic risk factors used previously  as abdominal obesity is an important component of the metabolic syndrome  but is closely related to BMI in this population. Lastly, there are no well-established definitions of adiposity for Latin America. Therefore, we used Caucasian cut-offs for BMI, which have not been validated in this population to determine which obesity-related cut-offs are most predictive of CVD-related morbidity and mortality in this population, including percentage body fat and waist circumference. BMI cut-offs still allow for internal comparisons as random error is expected to be equally distributed across groups. Prospective studies are needed to determine the risk associated with cardiovascular events in each metabolic group in this population.
Our study found that most Peruvians with overweight and obesity have additional risk factors for CVD, as well as a majority of those with a healthy weight. This number is only going to continue to increase unless national prevention programs are put in place to modify the behavioral and clinical risk factors, such as those proposed by the American Heart Association or simply exercise alone [39, 41]. Prevention programs aimed at individuals with a normal BMI, in addition to those who are overweight and obese, are urgently needed, such as screening for elevated fasting cholesterol and glucose. Understanding the differential metabolic responses to body size, its long-term consequences on hard outcomes and their potential to improve cardiovascular disease screening tools remain as areas of major challenges for cardiovascular disease prevention in low- and middle-income country settings.
The authors are indebted to all participants who kindly agreed to participate in the study. Special thanks to all field teams for their commitment and hard work, especially to Lilia Cabrera, Rosa Salirrosas, Viterbo Aybar, Sergio Mimbela, and David Danz for their leadership in each of the study sites, as well as Marco Varela for data coordination.
The members of the CRONICAS Cohort Study group are: Cardiovascular Disease: Antonio Bernabé-Ortiz, Juan P. Casas, George Davey Smith, Shah Ebrahim, Héctor H. García, Robert H. Gilman, Luis Huicho, Germán Málaga, J. Jaime Miranda, Víctor M. Montori, Liam Smeeth; Chronic Pulmonary Disease: William Checkley, Gregory B. Diette, Robert H. Gilman, Luis Huicho, Fabiola León-Velarde, María Rivera, Robert A. Wise; Training and Capacity Building: William Checkley, Héctor H. García, Robert H. Gilman, J. Jaime Miranda, Katherine Sacksteder.
Conceived and designed the experiments: ABO RHG WC LS GM JJM. Performed the experiments: ABO RHG WC LS GM JJM. Analyzed the data: CPB ABO. Wrote the paper: CPB ABO JJM.
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Dr Katherine Benziger is a true pioneer and leading expert in her field. Her work has for the past 30 years focused on the proper and ethical development and application of personality assessing in the global business environment. Significantly, Dr Benziger prefers the term personality assessing, rather than personality testing, to describe her approach. Katherine Benziger is keen to distance herself from the 'personality testing' industry, for which 'falsification of type', and the interests of the individual - rather than the organization - are not generally seen as a priority concerns. For Dr Benziger they are.
See also the Personality Models and Types section which includes more about Benziger's theory in relation to Jung, Myers Briggs, Eysenck, and other personality theories.
Also importantly, Benziger's systems are not psychometric tests. Many non-scientific people now use the term 'psychometrics' to cover the wide range of systems and tools used in testing, measuring and assessing all kinds of attributes in people, but strictly speaking this is incorrect. The term 'psychometrics' actually means the psychological theory or technique of mental measurement. Psychometrics and psychometric tests in this pure sense are often (and in certain countries necessarily) practised and administered only by people holding a PhD in psychology. This inherently can cause 'pure' psychometrics theory and testing tools to be less accessible for typical business and organisational applications.
Benziger's work, model and assessment systems are instead based on the measurement of brain function and energy consumption in the brain. This study of brain function is a different science, and a more recent one than psychology and psychometrics (the study of brain function has for instance been particularly aided by the advent of recent brain scanning technologies such as PET and MRI). The accessibility and application of Benziger's work and systems do not suffer the same restrictions and limitations as pure psychometrics, and as such offer potentially enormous benefits to organisations.
Benziger is keen to focus on the common tendency of people in work, whether being assessed or not, to 'falsify type'. She rightly says that when people adapt their natural thinking and working styles to fit expectations of others, normally created by work and career, tension and stress results. People are not happy and effective if they behave in unnatural ways, and much of Benziger's work focuses on dealing with these issues and the costs of falsifying.
Relating directly to this is the work Arlene Taylor PhD, a leading specialist in 'wellness' since 1980, and collaborator with Benziger for much of that time.
Arlene Taylor's work has confirmed, and builds on, Benziger's observations about the cost of falsifying type, notably the identification anecdotally of a collection of symptoms (in persons who were falsifying type) which Taylor has labelled Prolonged Adaption Stress Syndrome (PASS).
PASS initially featured in the 1999 Taylor and Benziger paper 'The Physiological Foundations of Falsification of Type and PASS', and remains central to Benziger's and Taylor's work.
The complete family of symptoms which Dr Arlene Taylor identified within PASS (Prolonged Adaption Stress Syndrome), as linked to Benziger's Falsification of Type, are:
- Immune system alterations
- Memory impairment
- Altered brain chemistry
- Diminished frontal lobe functions
- Discouragement and or depression
- Self-esteem problems
Benziger's principal assessment system is called the BTSA (Benziger Thinking Styles Assessment), and it's also available online as the eBTSA from the Benziger website, where you can learn more about Katherine Benziger and her ideas. I'd also strongly recommend you read Katherine's book, Thriving in Mind, available via her website. The book enables the reader to perform a basic personality assessment using the Benziger model, which is highly illuminating.
Here is a brief overview of Katherine Benziger's model: The brain has four specialised areas. Each is responsible for different brain functions (which imply strengths, behaviour and thinking style). The specialised areas are called 'modes'.
Each of us possesses natural strengths in only one of these specialised areas, which causes us to favour and use a certain style ahead of others. (Outside of that one style, we may have strengths and weaknesses which are based on what competencies we have been exposed to, or developed, and indeed which competencies we have not been exposed to.) Dr Benziger refers to the natural specialised area as the preferred thinking and behavioural mode. If you buy the book there's an excellent and simple assessment to illustrate this point, although it relies on complete honesty when answering - if you are 'falsifying your type' then you will distort the analysis.
Dr Benziger illustrates a person's brain dominance (preferences and tendencies) in terms of a brain diagram (viewed from above) when the relative strengths for each specialised area are plotted using scores from an assessment to produce a rhombus or kite shape. There is no right or wrong shape. The diagram is simply a way of visualising the bias of a person's brain, and the parts used more and better than the others.
Benziger's brain type model
Dr Benziger's model uses this representation of the brain (viewed from above, top is front) and the definitions below.
|mode||specialised area||brain functions||response to stimulus|
|1||basal left||Order and habit |
|Remembers definitions. What is, is as described.|
|2||basal right||Spiritual experience |
Rhythm and feeling
|Picks up emotional tone and the presence or absence of harmony (including harmony between people). What is, how we feel about it.|
|3||frontal right||Internal imaging |
Metaphor and imagination
|Sees the essence of things, in pictures and metaphors. What is, is something meaning or enabling something else.|
|4||frontal left||Structural analysis |
Prioritising and logic
|Converts into logical results or effects. What is, leads to, or produces results.|
Benziger says that people can have one and only one natural lead in which their brain is naturally efficient. They can and often do develop competencies in other modes. When they do in practice they will be using more areas of their brain, and when they do this the competencies outside their natural lead are always very draining.
Using the Benziger methodology and descriptions, here are some examples of brain types (which determine thinking and working styles), starting with the four modes and descriptions of each, shown as single-brain patterns. If you want to learn what your own thinking and working style is, get the book Thriving In Mind, or visit the Benziger website.
|Basal left - mode 1|
Strong basal left gives good routine, sequential, process skills. Detailed, structured, ordered, efficient, dependable, reliable, builds and maintains orderly foundations. Follows instructions, does things by the book, step-by-step. Communicates in writing, detailed.
Meets deadlines through following schedules and processes. Disciplined. Good attention to detail.
Can appear laboured, bureaucratic, or obstinate.
Basal right - mode 2
Strong basal right gives good abilities in intuition, feelings, empathy, relationships, connecting with people. Good active listening skills, understands how people feel, sensitive, picks up moods and feelings. Singing, dancing, speaking and listening with the eyes, touching, reaching out to people. Caring, compassionate. Non-verbally able, notices body-language. Interpersonally good, attentive to relationships and people. Internal language is feelings. Likes to harmonise with their environment. Can be a soft-touch, making too many personal sacrifices, and can find it difficult to say no. Doesn't like to upset people.
|Frontal right - mode 3|
Strong frontal right gives good spatial and internal imaging, innovating and adapting. Can visualise, conceptualise, (eg good at packing a car boot/trunk. Able to grasp whole pictures, themes, from vague outlines or ideas. relates to cartoons and models and caricatures. They file visually - where they can see things, in stacks.
Attentive to new ideas. Uses language to think out loud. Uses metaphors and word pictures. Expressive, at times looking within themselves to find or examine how best to paint the next word-picture. Enthusiastic and likes change. Gets bored. Can appear out of step, whacky, off-the-wall. Quirky sense of humour. At times to others can appear to have 'lost touch with reality'. Can change for change's sake. Good starters, not good finishers.
|Frontal left - mode 4|
Strong frontal left gives good analytical skills. Good at mathematics. Uses signage and labels to analyse and store data Physical and mental data storage. Nonemotional. Uses critical analysis to assess causes and effects, to make decisions and announce actions to meet goals. Makes judgements. Results orientated. Calculates and uses diagnostic thinking. Logical, good at verbal argument. tactics, goal-setting and goal achievement. Manages resources to achieve objectives. Uses operational principles.
Communicates in concise no-nonsense terms.
Can be seen as cold and manipulating, uncaring, unfeeling. Puts the task before people. Will bend rules. Will make new rules. Not strongly creative. Not good with people directly. Not strongly supportive or nurturing.
|Dual-brained - double left (modes 1 and 4)|
Strong frontal left and basal left skills.
|Dual-brained - double right (modes 2 and 3)|
Strong basal right and front right skills.
|Dual-brained - double frontal (modes 3 and 4)|
Strong frontal left and frontal right skills.
|Dual-brained - double basal (modes 1 and 2)|
Strong basal left and basal right skills.
Triple-brained pattern example
Skills of strong frontal right and double left.
The three other triple brain patterns:
bl/br/fr, br/fr/fl, fl/bl/br.
Triple-brained people are often 'translators', helping people with single or dual patterns to understand each other and co-operate.
Only 5% of people are whole-brained.
Strong in all four modes.
A 'translator', helping others to understand each other and co-operate, but can be prone to indecision, and can dramatically change direction of career or personal direction.
Benziger gives examples of jobs that are often comfortable with people who have developed a particular combination of modes. The list is by no means exhaustive:
|double lefts||lawyers, physicians, intensive care nurses|
|double lefts, with frontal left leads||accountants, MBA's, electrical engineers, hospital directors, implementer leaders,|
|double lefts with basal left leads||bankers, machine operators, machine repairers|
|basal lefts||ordering and purchasing clerks, record-keepers, filing clerks, book-keepers, personnel clerks, supervisors, head nurses, personnel officers, school administrators|
|basal rights||receptionists, communications specialists, pediatrics nurses, staff nurses, teachers, staff development specialists, trainers, community and public relations,|
|double basals||teachers, head nurses, supervisors|
|frontal rights||entrepreneurs, geologists, architects, illustrators, woodcraftsmen,|
|double rights||organisational development specialists, teachers, emergency doctors, dancers, painters, poets,|
|double rights with basal right leads||counsellors, psychologists, therapists, actors, musicians, interior decorators,|
|double rights with frontal right leads||counsellors, psychologists, therapists, psychiatrists|
|double frontals||inventors, chemists and chemical engineers, research scientists, economists, surgeons, hospital administrators, poets, composers, painters|
|basal left/frontal rights||journalists, librarians, community organisers,|
|triple-brain double right (right basal leads) with frontal lefts||poets, composers|
|triple-brain double left with frontal right leads||visionary leaders|
|whole-brained||leaders of large complex concerns|
Benziger model and other systems
Katherine Benziger makes several fascinating comparisons between the Benziger brain type model and other personality and behaviour systems:
Irwin Thompson's Archetypes in History (c 1970)
|Hunter||military general||frontal left|
|Leader||administrative leader||basal left|
|Shaman||spiritual leader||basal right|
|Fool||leader in impossible situations||front right|
DISC/Inscape/Thomas International/Performax etc (common usage in business since 1980s)
|Dominance||authoritative, decision-making, results-driven||double frontal, extraverted*|
|Influence||motivates, inspires, enthuses, leads, persuades||double right, extraverted|
|Steadiness||reliable, listens, follows routines and rules||double basal, introverted|
|Compliance||detailed, critical thinking, accurate||double left, introverted|
*See the Carl Jung definitions below of extraversion and introversion.
Carl Jung - Four Functions (c.1930)
|Thinking||analytic, objective, principles, standards, criteria, critiques||frontal left|
|Sensing||past, realistic, down-to-earth, practical, sensible||basal left|
|Feeling||subjective, personal, valuing intimacy, extenuating circumstances, humane, harmony||basal right|
|Intuition||hunches, futures, speculative, fantasy, imaginative||front right|
|Introversion||behaviour directed inwardly to understand and manage self and experience|
|Extraversion||behaviour directed externally, to influence outside factors and events|
Brain type, friendships, marriage and mating
Dr Benziger also makes interesting observations about relationships:
Most of us select friends who mirror our brain types. We do this because we feel comfortable with people whose mental preferences are like our own. If we find a friend with a near-identical brain type they are likely to become a 'best friend'.
The four most common brain developed patterns are: Double Basal, Double Left, Double Frontal and Double Right. As a rule people with such developed patterns find and make friends easiest, because there are simply more of them around than any other developed brain patterns. Single-brained people and multi-dominant triple- and whole-brained people find it more difficult to find friends, especially close friends because, simply there are not many people who have developed so many modes.
The search for a marriage and mating partner is different. Rather than try to 'mirror', we tend to choose marriage and mating partners with brain types that will complement our own, that will cover our weaknesses.
Understanding your own brain type, and therefore strengths and weaknesses, is helpful for self-development, managing relationships, managing teams, and generally being as fulfilled in life as we can be. Knowing your own strengths gives you confidence to take on responsibilities and projects in your own skill areas, and knowing your own weaknesses shows you where you need to seek help and advice.
The Brain Type model also explains very clearly that hardly anyone is good at everything, and even those who are, have other issues and challenges that result from their multi-skilled nature.
If you want to know more about Dr Benziger's theory visit Katherine Benziger's website, where more information and assessments are available.