Insulin Resistance in Non-Obese Patients: The “Metabolically Obese Normal Weight” Paradox

Introduction

The concept of metabolically obese normal weight (MONW) was introduced to describe individuals who have a body mass index (BMI) within the normal range but exhibit metabolic abnormalities that are typically associated with overweight or obesity. This phenotype challenged the long held assumption that a normal BMI is synonymous with good metabolic health. Although BMI remains a valuable screening tool for assessing body weight, it does not capture differences in body composition, fat distribution, or metabolic function. Consequently, some individuals with a normal BMI may harbor noteworthy cardiometabolic abnormalities that place them at increased risk of chronic disease despite appearing lean.

The MONW phenotype highlights the limitations of relying exclusively on anthropometric measurements when evaluating cardiometabolic risk. A normal BMI does not necessarily reflect a healthy metabolic profile, just as an elevated BMI does not always indicate metabolic dysfunction. Increasing evidence demonstrates that body fat distribution, particularly the accumulation of visceral adipose tissue, plays a more important role in metabolic health than total body weight alone. Individuals with excessive visceral fat may develop insulin resistance, chronic low grade inflammation, endothelial dysfunction, and ectopic fat deposition despite maintaining a normal body weight. These pathophysiological changes contribute to an increased risk of type 2 diabetes mellitus, cardiovascular disease, nonalcoholic fatty liver disease, chronic kidney disease, and premature mortality.

Despite its clinical relevance, MONW has not been established as a standardized medical diagnosis. One of the principal challenges surrounding the concept is the lack of a universally accepted definition. Investigators have used a variety of diagnostic criteria to identify this phenotype, including the presence of metabolic syndrome, evidence of insulin resistance, elevated body fat percentage, increased waist circumference or central adiposity, impaired glucose tolerance, dyslipidemia, hypertension, inflammatory biomarkers, or combinations of these metabolic abnormalities. Because these definitions vary considerably across studies, reported prevalence rates differ widely among populations and are difficult to compare. The absence of standardized diagnostic criteria also limits the development of uniform clinical guidelines and complicates epidemiological research.

Rather than focusing on assigning the label of metabolically obese normal weight, clinicians should adopt a broader and more practical approach centered on comprehensive cardiometabolic risk assessment. The primary objective is not to determine whether a patient fits a specific phenotype but to identify clinically significant metabolic dysfunction that warrants intervention. Patients with a normal BMI should therefore be evaluated for evidence of insulin resistance, impaired fasting glucose, prediabetes, type 2 diabetes mellitus, atherogenic dyslipidemia, hypertension, and other metabolic abnormalities that increase long term cardiovascular risk.

Assessment should also extend beyond traditional metabolic parameters to include evaluation of organ specific complications associated with metabolic dysfunction. Screening for chronic kidney disease through estimated glomerular filtration rate and urinary albumin excretion may identify early renal involvement. Liver health should be assessed for evidence of metabolic dysfunction associated steatotic liver disease and progressive fibrosis using appropriate laboratory tests and noninvasive fibrosis assessment tools when indicated. Depending on the patient’s age, family history, and overall risk profile, clinicians may also consider evaluation for subclinical atherosclerotic cardiovascular disease through advanced lipid testing, coronary artery calcium scoring, or other validated risk assessment strategies.

This patient centered approach aligns closely with the emerging cardiovascular kidney metabolic (CKM) syndrome framework, which recognizes that metabolic disorders are interconnected rather than isolated disease entities. CKM syndrome conceptualizes obesity, insulin resistance, type 2 diabetes mellitus, chronic kidney disease, hypertension, dyslipidemia, and cardiovascular disease as components of a continuous pathophysiological spectrum driven by shared metabolic mechanisms. Within this framework, adiposity is viewed not simply as excess body weight but as a complex biological process involving adipose tissue dysfunction, chronic inflammation, oxidative stress, endothelial injury, and neurohormonal dysregulation that collectively contribute to multisystem disease.

Importantly, the CKM framework shifts clinical attention away from BMI as the sole indicator of metabolic health. It encourages physicians to recognize that cardiometabolic risk exists along a continuum and may be present even in individuals who do not meet conventional definitions of overweight or obesity. This perspective supports earlier identification of high risk individuals and facilitates timely implementation of preventive strategies before irreversible cardiovascular or renal complications develop.

For practicing clinicians, this means that patients with a normal BMI should not automatically be considered metabolically healthy. A comprehensive evaluation should incorporate anthropometric measurements beyond BMI, including waist circumference and waist to height ratio, assessment of lifestyle factors such as physical activity, dietary habits, sleep quality, and smoking status, as well as detailed laboratory evaluation of glucose metabolism, lipid profiles, liver function, renal function, and blood pressure. When clinically appropriate, additional assessments of body composition or visceral adiposity may provide further insight into metabolic risk.

Ultimately, the value of the MONW concept lies less in its terminology than in the important clinical principle it conveys. Appearance alone is an unreliable indicator of metabolic health. Normal body weight does not exclude the presence of significant cardiometabolic disease, and overweight status does not invariably predict poor metabolic function. By adopting a comprehensive, risk based assessment that extends beyond BMI, clinicians can identify high risk individuals earlier, implement targeted preventive interventions, and improve long term cardiovascular, renal, and metabolic outcomes. This approach reflects the evolving understanding that metabolic health is determined by a complex interplay of body composition, adipose tissue function, insulin sensitivity, and systemic organ health rather than body weight alone.

Terminology: similar words, different meanings

Several terms are used in the literature, and they should not be collapsed into one diagnosis.

Metabolically obese normal weight usually refers to normal-BMI individuals with insulin resistance or metabolic syndrome features.

Metabolically unhealthy normal weight usually refers to normal-BMI individuals with abnormal glucose, triglycerides, HDL cholesterol, blood pressure, waist measures, or other metabolic risk markers.

Normal-weight obesity usually refers to normal BMI with elevated body fat percentage.

Normal-weight central obesity refers to normal BMI with increased waist circumference, waist-to-hip ratio, or waist-to-height ratio.

These differences matter at the bedside. A normal-weight patient with low muscle mass and high body fat percentage is not identical to a normal-weight patient with impaired glucose tolerance and normal body fat. Both may warrant evaluation, but the mechanism and management priorities may differ.

Why BMI can miss metabolic risk

Although body mass index (BMI) remains the most widely used screening tool for overweight and obesity, it has important limitations that reduce its ability to accurately assess metabolic health and cardiometabolic risk. BMI is calculated solely from an individual’s height and weight and does not distinguish between fat mass and lean body mass. Consequently, it provides only a crude estimate of body composition and fails to capture several biological factors that play a central role in the development of insulin resistance, type 2 diabetes, and cardiovascular disease. As understanding of metabolic dysfunction has advanced, it has become increasingly clear that BMI alone is an inadequate measure of an individual’s true metabolic risk.

One of the most crucial limitations of BMI is its inability to assess body fat distribution. The location of adipose tissue is often more clinically relevant than the total quantity of body fat. Visceral adipose tissue, which accumulates within the abdominal cavity around internal organs, is metabolically active and strongly associated with insulin resistance, chronic low grade inflammation, dyslipidemia, hypertension, and increased cardiovascular risk. Unlike subcutaneous fat, visceral fat releases proinflammatory cytokines, free fatty acids, and adipokines that contribute directly to metabolic dysfunction. As a result, individuals with relatively normal body weight may still have substantial visceral fat accumulation and experience marked metabolic abnormalities despite having a BMI within the normal range.

This phenomenon is particularly evident in individuals with central obesity or increased abdominal adiposity despite an apparently healthy body weight. Such patients may have excessive visceral or hepatic fat while maintaining a normal BMI, placing them at elevated risk for type 2 diabetes, metabolic syndrome, and cardiovascular disease. Waist circumference, waist to height ratio, and advanced body composition assessments often provide a more accurate evaluation of these individuals than BMI alone. These measures better reflect abdominal fat distribution and have demonstrated stronger associations with adverse metabolic outcomes.

Ethnic and ancestral differences further illustrate the limitations of BMI as a universal marker of health. Numerous studies have shown that South Asian and East Asian populations tend to accumulate visceral fat at lower BMI values compared with individuals of European ancestry. Consequently, these populations often develop insulin resistance, type 2 diabetes, and cardiovascular disease at BMI thresholds traditionally considered normal or only mildly elevated. This observation has prompted several international organizations to recommend lower BMI cutoffs for identifying overweight and obesity in specific populations. Relying exclusively on conventional BMI thresholds may therefore delay diagnosis and intervention in individuals who are already metabolically compromised.

BMI also fails to account for skeletal muscle mass, muscle strength, and muscle quality, all of which are essential determinants of glucose metabolism. Skeletal muscle is responsible for the majority of insulin mediated glucose uptake following meals and serves as the body’s largest reservoir for glucose disposal. Individuals with reduced muscle mass or impaired muscle function have diminished capacity to clear circulating glucose, increasing the likelihood of insulin resistance even when body weight appears normal. This condition, often referred to as sarcopenia or sarcopenic obesity when accompanied by excess fat mass, is increasingly recognized as an important contributor to metabolic disease.

The importance of skeletal muscle becomes even greater with advancing age and in individuals exposed to factors that accelerate muscle loss. Aging is associated with progressive declines in muscle mass and strength, while chronic illnesses, prolonged physical inactivity, glucocorticoid therapy, menopause, and inadequate protein intake further exacerbate muscle deterioration. These physiological changes reduce insulin sensitivity and impair metabolic flexibility, increasing the risk of glucose intolerance independently of BMI. Consequently, two individuals with identical BMI values may have markedly different metabolic profiles depending on their muscle composition and functional capacity.

Physical fitness represents another critical determinant of metabolic health that BMI cannot measure. Cardiorespiratory fitness and regular physical activity improve insulin sensitivity, reduce systemic inflammation, enhance mitochondrial function, and lower cardiovascular risk regardless of body weight. Individuals with higher levels of physical fitness often demonstrate better metabolic health than sedentary individuals with similar BMI values. This distinction highlights the importance of evaluating lifestyle factors alongside anthropometric measurements when assessing overall health risk.

Another major limitation of BMI is its inability to detect ectopic fat accumulation. Ectopic fat refers to lipid deposition in tissues not specialized for fat storage, including the liver, pancreas, skeletal muscle, myocardium, and other organs. Unlike subcutaneous fat, ectopic fat directly interferes with organ function and contributes substantially to metabolic dysfunction. Hepatic steatosis, pancreatic fat infiltration, and intramyocellular lipid accumulation have all been linked to insulin resistance, impaired beta cell function, and progression toward type 2 diabetes. Importantly, these abnormalities may develop in individuals who do not meet conventional definitions of overweight or obesity.

Metabolic dysfunction associated steatotic liver disease (MASLD) illustrates this concept particularly well. Many patients with MASLD have normal or only modestly elevated BMI but exhibit marked hepatic fat accumulation accompanied by insulin resistance, dyslipidemia, and systemic inflammation. In such cases, fatty liver serves as an important clinical indicator that normal body weight does not necessarily equate to metabolic health. Recognition of MASLD should prompt clinicians to evaluate patients more comprehensively for underlying cardiometabolic risk factors, regardless of BMI classification.

Beyond body composition, BMI does not adequately account for several biological variables that influence metabolic risk. Age, sex, menopausal status, hormonal changes, medication exposure, genetic predisposition, inflammatory biology, and differences in insulin sensitivity all contribute to an individual’s metabolic profile but are not reflected in BMI calculations. For example, menopause is associated with redistribution of body fat toward the abdomen and increased visceral adiposity despite relatively stable body weight. Similarly, medications such as glucocorticoids, atypical antipsychotics, and certain antidiabetic therapies may alter fat distribution and insulin sensitivity independently of changes in BMI.

These limitations underscore the need for a more comprehensive approach to metabolic risk assessment. Rather than relying solely on BMI, clinicians should integrate multiple clinical and laboratory parameters, including waist circumference, body composition analysis, blood pressure, fasting glucose, glycated hemoglobin, lipid profile, liver function tests, physical activity levels, muscle strength, and family history. Where appropriate, imaging modalities such as dual energy X ray absorptiometry, computed tomography, magnetic resonance imaging, or transient elastography can provide valuable information regarding fat distribution and ectopic fat deposition.

In summary, while BMI remains a practical and accessible screening tool, it should not be regarded as a definitive measure of metabolic health. Its inability to evaluate fat distribution, visceral adiposity, ectopic fat accumulation, skeletal muscle mass, physical fitness, insulin sensitivity, inflammatory status, and individual biological variation limits its clinical utility when used in isolation. A more nuanced, multidimensional assessment provides a substantially more accurate evaluation of metabolic risk and enables earlier identification of individuals who may benefit from targeted preventive and therapeutic interventions, even when their BMI falls within the normal range.

Pathophysiology

Insulin resistance in normal-weight patients is usually multifactorial. No single mechanism explains every case.

Central adiposity may contribute through increased fatty acid flux, hepatic insulin resistance, altered adipokine signaling, and inflammatory activation. These pathways are biologically plausible and well described in obesity, but clinicians should avoid implying that every normal-BMI patient has the same mechanism.

Low muscle mass and reduced muscle quality may also contribute. Skeletal muscle is the major site of insulin-mediated glucose disposal after meals. When muscle mass or function declines, glucose handling can worsen even without weight gain.

Genetic and early-life factors may influence susceptibility. Family history of type 2 diabetes, prior gestational diabetes, PCOS, premature menopause, high-risk ancestry, and early-life growth patterns may all modify risk. These factors should guide screening, but they should not replace individualized assessment.

Medication exposure is frequently overlooked. Glucocorticoids, some second-generation antipsychotics, some antiretroviral regimens, calcineurin inhibitors, and other agents can worsen glucose metabolism or lipid profiles. A normal BMI does not protect patients from these effects.

Sleep and circadian disruption can also worsen insulin sensitivity. Short sleep, shift work, irregular sleep timing, and obstructive sleep apnea may contribute to hypertension, dysglycemia, appetite dysregulation, and cardiometabolic risk.

Clinical clues

Normal-BMI patients deserve closer metabolic evaluation when risk clues are present. These include central adiposity, hypertension, hypertriglyceridemia, low HDL cholesterol, impaired fasting glucose, elevated A1c, MASLD, PCOS, prior gestational diabetes, obstructive sleep apnea, family history of premature ASCVD, family history of type 2 diabetes, sedentary lifestyle, sarcopenia, chronic glucocorticoid therapy, or exposure to metabolically active psychiatric medications.

The physical examination should include waist circumference when cardiometabolic risk is suspected. Waist-to-height ratio can also be useful because it relates waist size to body size. Blood pressure should be measured carefully, using validated technique, because normal BMI should not lower vigilance for hypertension.

Skin and endocrine findings may provide additional clues. Acanthosis nigricans, skin tags, androgen excess, proximal muscle weakness, easy bruising, or rapid central fat redistribution should prompt targeted evaluation. Broad endocrine testing is not necessary for every patient, but unusual findings should not be ignored.

Practical assessment

The goal is not to diagnose MONW as a stand-alone disease. The goal is to identify clinically actionable risk.

Initial assessment should include personal history, family history, medication review, dietary pattern, physical activity, alcohol intake, smoking status, sleep history, reproductive history, and prior cardiometabolic test results. This is where many missed cases are found. A patient with normal BMI, high triglycerides, low HDL cholesterol, and a father with premature myocardial infarction should not be reassured by weight alone.

Laboratory testing should be guided by risk. A reasonable initial assessment includes A1c or fasting plasma glucose, lipid panel, serum creatinine with eGFR, and liver enzymes when MASLD risk is present. Urine albumin-to-creatinine ratio is appropriate when diabetes, hypertension, chronic kidney disease, or broader CKM risk is present.

OGTT is not needed for every patient. It can be helpful when fasting glucose and A1c are nondiagnostic but suspicion remains high, such as prior gestational diabetes, PCOS, high-risk ancestry, suspected postprandial dysglycemia, or discordant clinical findings.

Fasting insulin and HOMA-IR are not routine diagnostic tools in primary care. HOMA-IR cutoffs vary by assay, age, sex, ancestry, and study population. A threshold such as 2.5 should not be presented as a universal clinical standard.

Advanced risk testing should be selective. ApoB can help when LDL cholesterol and triglyceride patterns are discordant or when atherogenic particle burden is uncertain. Lp(a) testing is useful at least once in adulthood in many contemporary lipid-risk frameworks, especially when family history suggests inherited risk. Coronary artery calcium scoring can help clarify statin decisions in selected borderline or intermediate-risk patients.

Table 1. Initial assessment in a normal-BMI patient with suspected metabolic risk

Area	What to assess	Clinical use
Body distribution	Waist circumference or waist-to-height ratio	Detects central adiposity missed by BMI
Glucose	A1c, fasting glucose, selected OGTT	Identifies prediabetes, diabetes, or postprandial dysglycemia
Lipids	LDL-C, HDL-C, triglycerides, non-HDL-C	Detects atherogenic dyslipidemia
Particle risk	ApoB when discordant risk is suspected	Clarifies atherogenic particle burden
Inherited risk	Family history, Lp(a) when appropriate	Identifies risk not reflected by BMI
Blood pressure	Office and home BP when needed	Detects hypertension or masked hypertension
Liver	ALT, AST, fibrosis risk tools when MASLD suspected	Screens for steatotic liver disease and fibrosis risk
Kidney	eGFR, urine albumin-to-creatinine ratio when indicated	Identifies CKM risk
Muscle	Strength, function, activity level, DXA when needed	Detects sarcopenia or low lean mass
Medications	Steroids, antipsychotics, antiretrovirals, calcineurin inhibitors	Identifies reversible contributors

Cardiovascular risk

Normal-weight metabolic dysfunction is associated with higher cardiovascular risk than metabolically healthy normal weight. The risk is not caused by BMI category itself. It is driven by measurable factors: blood pressure, atherogenic lipoproteins, glycemia, kidney disease, smoking, family history, central adiposity, and systemic metabolic dysfunction.

Risk assessment should use contemporary ASCVD risk tools and guideline-directed interpretation. Normal BMI should not prevent statin consideration when LDL-C, diabetes, chronic kidney disease, Lp(a), ApoB, CAC, or estimated ASCVD risk supports treatment.

The reverse is also true. A normal-weight patient should not receive drug therapy simply because the term MONW is applied. Treatment should follow the abnormality: hypertension gets hypertension treatment, dyslipidemia gets lipid-risk management, diabetes gets diabetes care, and MASLD with fibrosis risk gets liver-directed evaluation.

Diabetes risk

Insulin resistance may precede type 2 diabetes, but progression is not inevitable. Some patients remain stable. Others improve with exercise, dietary change, sleep optimization, medication adjustment, and treatment of comorbid conditions. The article should avoid claiming that most MONW patients progress to diabetes within a fixed number of years unless a specific cohort is cited.

Screening should follow current diabetes guidance, with clinical judgment for high-risk normal-BMI patients. A1c and fasting glucose are practical first tests. OGTT is more sensitive for impaired glucose tolerance and may be useful when suspicion remains high.

Metformin may be considered for selected high-risk patients with prediabetes, especially when risk is substantial. It should not be used solely for “insulin resistance” in a normal-weight patient without dysglycemia or another clear indication. Before prescribing, clinicians should assess renal function, review gastrointestinal tolerability, evaluate drug interactions and alcohol use, and consider vitamin B12 monitoring during long-term therapy.

MASLD in non-obese patients

The older term NAFLD is increasingly being replaced by metabolic dysfunction-associated steatotic liver disease, or MASLD. This terminology better reflects the metabolic context of hepatic steatosis.

MASLD can occur without obesity. Risk is higher when central adiposity, prediabetes, type 2 diabetes, hypertriglyceridemia, hypertension, or high-risk ancestry is present. Normal aminotransferases do not exclude clinically important fibrosis.

When MASLD is suspected, the main clinical question is not only whether fat is present in the liver. The more important question is whether the patient has fibrosis risk. Noninvasive fibrosis tools and elastography can help determine who needs closer follow-up or hepatology referral.

Management principles

Management should target cardiometabolic function, not appearance. In a normal-BMI patient, indiscriminate calorie restriction may worsen low muscle mass and fail to address the underlying risk pattern.

Dietary intervention should emphasize quality. Practical goals include reducing refined carbohydrates and sugar-sweetened beverages, increasing fiber-rich foods, replacing trans fats and excessive saturated fat with unsaturated fat sources, moderating alcohol intake, and ensuring adequate protein intake when sarcopenia risk is present.

Resistance training is particularly important. It supports muscle mass, strength, glucose disposal, and functional reserve. Aerobic exercise improves insulin sensitivity, triglycerides, blood pressure, and cardiorespiratory fitness. A combined approach is usually best.

Sedentary time deserves specific counseling. A patient may meet exercise targets but still sit most of the day. Short walking breaks and post-meal activity can improve postprandial glucose patterns and are easier for many patients to adopt than a complete exercise overhaul.

Sleep should be part of metabolic care. Short sleep, shift work, and obstructive sleep apnea may worsen glycemia and blood pressure. Normal BMI does not exclude sleep apnea, especially when hypertension, snoring, witnessed apneas, or daytime sleepiness are present.

Table 2. Management priorities for normal-BMI metabolic risk

Problem	First-line focus	Escalation
Central adiposity	Resistance plus aerobic training	Consider body composition follow-up
Prediabetes	Lifestyle intervention, activity, sleep	Consider metformin in selected high-risk patients
Atherogenic dyslipidemia	Diet quality, ASCVD risk assessment	Statin or other lipid therapy when indicated
Hypertension	Confirm BP, sodium moderation, activity	Guideline-directed antihypertensive therapy
Low muscle mass	Protein adequacy, resistance training	Evaluate frailty, hypogonadism, malnutrition, or chronic disease
MASLD risk	Weight-neutral metabolic intervention	Fibrosis risk assessment and hepatology referral when indicated
Medication-related risk	Review culprit medications	Substitute or monitor when clinically feasible

Pharmacology and safety considerations

Medication decisions should be indication-based. The MONW label is not itself an indication for metformin, statins, antihypertensives, GLP-1 receptor agonists, or obesity medications.

Metformin is appropriate for type 2 diabetes and may be considered in selected high-risk prediabetes. It requires renal assessment before initiation and periodic monitoring afterward. It is contraindicated when eGFR is below 30 mL/min/1.73 m², and initiation is generally not recommended when eGFR is 30 to 45 mL/min/1.73 m². Long-term use can reduce vitamin B12 levels.

Statins should be prescribed according to ASCVD risk, LDL-C level, diabetes status, CKD status, CAC findings, and risk-enhancing factors. They should not be withheld because a patient is normal weight. They also should not be prescribed merely because a clinician suspects insulin resistance. Monitor for drug interactions, muscle symptoms, hepatic symptoms, pregnancy considerations, and adherence.

ACE inhibitors and ARBs are useful when standard indications are present, including hypertension, albuminuria, CKD, heart failure, or ASCVD-risk contexts. They should not be described as insulin-sensitizing therapy. Monitor creatinine and potassium, and avoid use in pregnancy.

Thiazide diuretics and beta-blockers can modestly affect glucose metabolism in some patients. This does not make them inappropriate when indicated. The best approach is thoughtful drug selection, dose awareness, and monitoring.

Glucocorticoids can worsen postprandial glucose, raise hepatic glucose output, promote central fat accumulation, and contribute to muscle loss. When possible, use the lowest effective dose and duration. In high-risk patients, consider glucose monitoring during systemic steroid therapy.

Second-generation antipsychotics differ substantially in metabolic risk. Clozapine and olanzapine generally carry higher risk, while agents such as aripiprazole, ziprasidone, and lurasidone tend to have lower average metabolic liability. Individual response varies, so baseline and follow-up metabolic monitoring remain important.

Table 3. Medication-safety issues to include

Drug or class	Main issue	Monitoring
Metformin	Renal limits, lactic acidosis risk, B12 deficiency	eGFR, GI tolerance, B12 when long-term or symptomatic
Statins	Myopathy symptoms, liver symptoms, interactions	Lipid response, adherence, symptoms, interaction review
ACE inhibitors or ARBs	Hyperkalemia, creatinine rise, fetal toxicity	Creatinine, potassium, pregnancy status
Thiazides	Glucose, potassium, uric acid effects	BP, electrolytes, glucose when risk is high
Beta-blockers	Glycemic effects vary by agent	Glucose monitoring when clinically relevant
Glucocorticoids	Hyperglycemia, central fat gain, muscle loss	Postprandial glucose in high-risk patients
Antipsychotics	Weight, glucose, lipids, BP	Baseline and periodic metabolic monitoring

Follow-up

Follow-up intervals should reflect baseline abnormalities and risk trajectory. Patients with normal initial glycemic testing but strong risk factors can generally be retested every 1 to 3 years, depending on clinical context. Patients with prediabetes usually warrant at least annual glycemic follow-up.

Blood pressure should be assessed at routine visits. Home monitoring is useful when office readings are borderline, variable, or suspected to reflect white-coat or masked hypertension.

Lipids should be followed according to ASCVD risk and therapy status. If statin therapy is started, reassess response and adherence after initiation or dose adjustment.

Patients with suspected MASLD should not be followed by aminotransferases alone. Fibrosis risk assessment should guide follow-up intensity and referral decisions.

For patients with low muscle mass or functional decline, weight should not be the main outcome. Strength, gait speed, chair-rise performance, resistance-training adherence, protein intake, and fall risk may be more clinically meaningful.

Practical clinician takeaways

A normal body mass index (BMI) should not be considered sufficient evidence of cardiometabolic health, particularly when other clinical risk indicators are present. Although BMI remains a widely used screening tool because of its simplicity and accessibility, it is an imperfect measure that does not distinguish between fat mass and lean body mass, nor does it provide information about fat distribution or metabolic function. Consequently, individuals with a BMI within the normal range may still harbor significant metabolic abnormalities that increase their risk of cardiovascular disease, type 2 diabetes, chronic kidney disease, nonalcoholic fatty liver disease, and premature mortality. Clinicians should therefore avoid relying solely on BMI when evaluating cardiometabolic risk and instead adopt a more comprehensive, individualized assessment.

One important concept in this context is metabolically obese normal weight (MONW). MONW describes individuals who have a normal BMI but exhibit metabolic characteristics commonly associated with obesity, including insulin resistance, visceral adiposity, dyslipidemia, hypertension, and chronic low grade inflammation. It is important to recognize that MONW is currently best regarded as a research phenotype rather than a formally established clinical diagnosis. There are no universally accepted diagnostic criteria, and definitions vary across epidemiological studies and research populations. Nevertheless, the phenotype highlights an important clinical reality that excess metabolic risk may exist even in the absence of elevated body weight.

Because BMI alone frequently fails to identify these patients, clinicians should expand cardiometabolic evaluation whenever additional risk factors or clinical clues are present. Measurement of waist circumference and waist to height ratio provides valuable information regarding central adiposity, which is more strongly associated with insulin resistance and cardiovascular risk than overall body weight. Assessment of glucose metabolism through fasting plasma glucose and glycated hemoglobin testing can identify impaired glucose regulation or previously unrecognized diabetes. Lipid evaluation should extend beyond total cholesterol to include triglycerides, high density lipoprotein cholesterol, and low density lipoprotein cholesterol, as the combination of elevated triglycerides and reduced high density lipoprotein cholesterol is often indicative of underlying metabolic dysfunction.

Blood pressure measurement remains an essential component of cardiometabolic assessment, as even mild elevations may reflect early vascular dysfunction. A thorough medication review is equally important because certain drugs, including glucocorticoids, atypical antipsychotics, some antiretroviral therapies, and hormonal treatments, can contribute to insulin resistance, weight redistribution, or dyslipidemia. Family history should be carefully explored to identify inherited susceptibility to premature cardiovascular disease, diabetes, dyslipidemia, or hypertension, particularly when metabolic abnormalities develop in individuals with otherwise normal body weight.

A comprehensive evaluation should also consider organ specific manifestations of metabolic dysfunction. Screening for chronic kidney disease using estimated glomerular filtration rate and urine albumin to creatinine ratio can identify early renal involvement. Assessment for metabolic dysfunction associated steatotic liver disease through liver enzyme testing and appropriate imaging when indicated is also important, as hepatic steatosis frequently develops in individuals with insulin resistance regardless of BMI. In addition, clinicians should evaluate for sleep disorders, particularly obstructive sleep apnea, which contributes to metabolic dysregulation through intermittent hypoxia, sympathetic activation, and systemic inflammation.

Assessment of physical function and body composition should not be overlooked. Reduced skeletal muscle mass, impaired muscle strength, and diminished cardiorespiratory fitness may coexist with normal body weight and substantially increase cardiometabolic risk. Functional measures such as grip strength, gait speed, and exercise capacity provide valuable information regarding overall metabolic health and may reveal vulnerabilities that are not apparent through anthropometric measurements alone.

Although insulin resistance represents a central mechanism underlying the MONW phenotype, routine measurement using the Homeostatic Model Assessment of Insulin Resistance (HOMA IR) is generally not recommended in standard clinical practice. HOMA IR remains primarily a research tool because insulin assays lack standardization across laboratories and no universally accepted diagnostic cutoff has been established. Consequently, clinicians should avoid assigning fixed threshold values or relying on HOMA IR as a routine diagnostic requirement. Instead, metabolic risk should be assessed using validated clinical and biochemical parameters that are widely available and supported by current practice guidelines.

Management should be individualized and based on the patient’s overall cardiometabolic risk profile rather than body weight alone. A weight neutral approach is often appropriate, particularly for individuals with normal BMI who exhibit metabolic abnormalities. The primary therapeutic objective should be improvement in metabolic health rather than weight reduction itself. Lifestyle interventions should emphasize increasing skeletal muscle mass through progressive resistance training, improving cardiorespiratory fitness with regular aerobic exercise, enhancing dietary quality by prioritizing nutrient dense whole foods, reducing sedentary behavior, and optimizing sleep duration and quality. These interventions have consistently demonstrated benefits in improving insulin sensitivity, reducing visceral adiposity, enhancing lipid profiles, and lowering cardiovascular risk independent of changes in body weight.

Pharmacologic therapy should be guided by established clinical indications rather than BMI alone. Lipid lowering agents, antihypertensive medications, glucose lowering therapies, and other evidence based treatments should be initiated according to current guideline recommendations for patients with documented dyslipidemia, hypertension, diabetes, or other cardiometabolic disorders. This risk based approach ensures that treatment decisions are driven by objective measures of disease rather than anthropometric classifications.

In summary, normal BMI should never be interpreted as definitive evidence of metabolic health when additional clinical risk factors are present. The concept of metabolically obese normal weight underscores the limitations of BMI as a solitary screening tool and reinforces the need for comprehensive cardiometabolic evaluation. By incorporating measures of central adiposity, metabolic biomarkers, organ specific risk assessment, physical function, and lifestyle factors into routine practice, clinicians can identify high risk individuals who might otherwise remain undiagnosed. A comprehensive, risk based, and weight neutral management strategy provides the most effective framework for reducing long term cardiometabolic complications while recognizing that metabolic health extends far beyond body weight alone.

References

1. American Diabetes Association Professional Practice Committee for Diabetes. (2026). 2. Diagnosis and classification of diabetes: Standards of Care in Diabetes, 2026. Diabetes Care, 49(Suppl. 1), S27-S49. doi:10.2337/dc26-S002
2. American Diabetes Association Professional Practice Committee for Diabetes. (2026). 3. Prevention or delay of diabetes and associated comorbidities: Standards of Care in Diabetes, 2026. Diabetes Care, 49(Suppl. 1), S50-S60. doi:10.2337/dc26-S003
3. Aung, K. K., Lorenzo, C., Hinojosa, M. A., & Haffner, S. M. (2014). Risk of developing diabetes and cardiovascular disease in metabolically unhealthy normal-weight and metabolically healthy obese individuals. Journal of Clinical Endocrinology & Metabolism, 99(2), 462-468. doi:10.1210/jc.2013-2832
4. Blumenthal, R. S., Morris, P. B., Gaudino, M., Johnson, H. M., Anderson, T. S., Bittner, V. A., Blankstein, R., Brewer, L. C., Cho, L., de Ferranti, S. D., Gianos, E., Gluckman, T. J., Gradney, K. F., Isiadinso, I., Lloyd-Jones, D. M., Marrs, J. C., Martin, S. S., McLain, K. H., Mehta, L. S., … Wilkins, J. T. (2026). 2026 ACC/AHA/AACVPR/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA guideline on the management of dyslipidemia: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. Advance online publication. doi:10.1161/CIR.0000000000001423
5. Conus, F., Allison, D. B., Rabasa-Lhoret, R., St-Onge, M., Tremblay-Lebeau, A., Weisnagel, S. J., & Poehlman, E. T. (2004). Metabolic and behavioral characteristics of metabolically obese but normal-weight women. Journal of Clinical Endocrinology & Metabolism, 89(10), 5013-5020. doi:10.1210/jc.2004-0265
6. DailyMed. (n.d.). Metformin hydrochloride tablets: Prescribing information. U.S. National Library of Medicine.
7. Meigs, J. B., Wilson, P. W. F., Fox, C. S., Vasan, R. S., Nathan, D. M., Sullivan, L. M., & D’Agostino, R. B. (2006). Body mass index, metabolic syndrome, and risk of type 2 diabetes or cardiovascular disease. Journal of Clinical Endocrinology & Metabolism, 91(8), 2906-2912. doi:10.1210/jc.2006-0594
8. Ndumele, C. E., Rodriguez, F., Dixon, D. L., Khan, S. S., Mukherjee, D., Bajaj, M., Bangalore, S., Bozkurt, B., Breathett, K., Clarke, S. L., de Boer, I. H., Ellison, D. H., Evangelista, L. S., Heffron, S. P., Kazi, D. S., Kulshreshtha, A., Lingvay, I., Low Wang, C. C., Mercado, C. A., … Virani, S. S. (2026). 2026 AHA/ACC/ADA/ASN guideline for the prevention, detection, evaluation, and management of cardiovascular-kidney-metabolic syndrome: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. Advance online publication. doi:10.1161/CIR.0000000000001453
9. Piercy, K. L., Troiano, R. P., Ballard, R. M., Carlson, S. A., Fulton, J. E., Galuska, D. A., George, S. M., & Olson, R. D. (2018). The Physical Activity Guidelines for Americans. JAMA, 320(19), 2020-2028. doi:10.1001/jama.2018.14854
10. Rinella, M. E., Lazarus, J. V., Ratziu, V., Francque, S. M., Sanyal, A. J., Kanwal, F., Romero, D., Abdelmalek, M. F., Anstee, Q. M., Arab, J. P., Arrese, M., Bataller, R., Beuers, U., Boursier, J., Bugianesi, E., Byrne, C. D., … NAFLD Nomenclature Consensus Group. (2023). A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology, 78(6), 1966-1986. doi:10.1097/HEP.0000000000000520
11. Rinella, M. E., Neuschwander-Tetri, B. A., Siddiqui, M. S., Abdelmalek, M. F., Caldwell, S., Barb, D., Kleiner, D. E., & Loomba, R. (2023). AASLD practice guidance on the clinical assessment and management of nonalcoholic fatty liver disease. Hepatology, 77(5), 1797-1835. doi:10.1097/HEP.0000000000000323
12. Romero-Corral, A., Somers, V. K., Sierra-Johnson, J., Korenfeld, Y., Boarin, S., Korinek, J., Jensen, M. D., Parati, G., & Lopez-Jimenez, F. (2010). Normal weight obesity: A risk factor for cardiometabolic dysregulation and cardiovascular mortality. European Heart Journal, 31(6), 737-746. doi:10.1093/eurheartj/ehp487
13. Ruderman, N., Chisholm, D., Pi-Sunyer, X., & Schneider, S. (1998). The metabolically obese, normal-weight individual revisited. Diabetes, 47(5), 699-713. doi:10.2337/diabetes.47.5.699
14. Sahakyan, K. R., Somers, V. K., Rodriguez-Escudero, J. P., Hodge, D. O., Carter, R. E., Sochor, O., Coutinho, T., Jensen, M. D., Roger, V. L., Singh, P., & Lopez-Jimenez, F. (2015). Normal-weight central obesity: Implications for total and cardiovascular mortality. Annals of Internal Medicine, 163(11), 827-835. doi:10.7326/M14-2525
15. Stefan, N., Schick, F., & Häring, H.-U. (2017). Causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metabolism, 26(2), 292-300. doi:10.1016/j.cmet.2017.07.008
16. St-Onge, M.-P., Janssen, I., & Heymsfield, S. B. (2004). Metabolic syndrome in normal-weight Americans: New definition of the metabolically obese, normal-weight individual. Diabetes Care, 27(9), 2222-2228. doi:10.2337/diacare.27.9.2222
17. U.S. Department of Health and Human Services. (2018). Physical Activity Guidelines for Americans (2nd ed.).
18. U.S. Department of Health and Human Services & U.S. Department of Agriculture. (2026). Dietary Guidelines for Americans, 2025-2030.
19. U.S. Food and Drug Administration. (2021, July 20). Statins: Drug safety communication. FDA requests removal of strongest warning against using cholesterol-lowering statins during pregnancy.
20. U.S. Preventive Services Task Force. (2021). Screening for prediabetes and type 2 diabetes: U.S. Preventive Services Task Force recommendation statement. JAMA, 326(8), 736-743. doi:10.1001/jama.2021.12531
21. Wildman, R. P., Muntner, P., Reynolds, K., McGinn, A. P., Rajpathak, S., Wylie-Rosett, J., & Sowers, M. R. (2008). The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: Prevalence and correlates of 2 phenotypes among the U.S. population. Archives of Internal Medicine, 168(15), 1617-1624. doi:10.1001/archinte.168.15.1617