Obesity, Polymorbidity, and Polypharmacy: What the Data Gets Right and What It Misses

The Chen et al. NHANES analysis correctly identifies obesity as a measurable, quantifiable contributor to polypharmacy in older US adults, accounting for 14.8% of cases by BMI and 24.8% by waist circumference. This is a genuine methodological strength: population-attributable fraction analysis gives clinicians and policymakers a number to act on, rather than a vague association. The finding that waist circumference captures a larger obesity prevalence than BMI alone also correctly flags that central adiposity is a better polypharmacy predictor than total body mass, likely because visceral fat drives insulin resistance and inflammatory burden more directly.

Where the Framing Falls Short

The study's central limitation is scope, not method: it never establishes a comparison arm of non-obese older adults stratified by polymorbidity, so it cannot tell us whether a lean patient with four chronic conditions carries a similar or greater medication burden than an obese patient with two. Existing multimorbidity literature suggests this comparison would matter — a large meta-analysis pooling over 100 studies found polypharmacy prevalence driven primarily by chronic condition count, not any single risk factor like obesity. Related work in African American older adults found multimorbidity functions as the mediator between obesity and polypharmacy — meaning obesity's effect is almost entirely indirect, funneled through the diseases it causes, not a standalone pharmacologic driver. The Chen study's own framing acknowledges this mechanism in passing but never tests it directly, leaving an open question: if two patients have identical comorbidity scores, does obesity add anything to medication count beyond what the comorbidities already predict? Without that comparison, the "1 in 7" headline risks overstating obesity's independent role relative to polymorbidity itself, the more clinically actionable variable in many patients.

The Missing Argument: The Body's Own Restorative Capacity

A structural blind spot in this style of cross-sectional research is that it treats medication count as a static outcome rather than a moving target tied to a dynamic, self-correcting physiology. Older adults' organ systems retain real capacity for partial recovery once a dominant driver of dysfunction is controlled — blood pressure, glucose, and lipid parameters frequently improve after weight loss, better sleep, or resolution of inflammation, independent of added drugs. The 2025 EASO position statement on obesity in older adults makes this point directly, recommending multimodal management (exercise, protein-adequate calorie restriction, obesity medications) precisely because it preserves function and can reduce the downstream medication burden obesity itself created. This reframes the clinical goal: rather than treating each diagnosis in isolation and adding drugs indefinitely, clinicians should periodically ask whether the original indication for a medication still exists — a concept formally described as "legacy prescribing,"where drugs started for a defined problem get continued indefinitely without re-evaluation.

Recommendations: A Stepwise Framework to Prevent and Reverse Polypharmacy

Step	Action	Rationale
1. Rank severity	Identify the single condition most threatening near-term function or survival	Prioritization is described as an essential skillsince guidelines address starting drugs, rarely stopping them
2. Treat minimally	Use the lowest effective dose/regimen for that top driver first	Avoids reflexive multi-drug initiation before assessing single-driver response
3. Reassess on a schedule	Re-evaluate physiologic markers in weeks, not months	Captures the body's restorative response before assuming permanence
4. Identify deprescribing candidates	Flag legacy prescriptions (PPIs, SSRIs, benzodiazepines) never re-reviewed after their intended duration	Formal deprescribing clinical practice guidelines now exist specifically for this purpose
5. Taper, don't stop abruptly	Plan gradual dose reduction with monitoring for recurrence or withdrawal effects	Five-step deprescribing process: identify, reduce/stop, taper, monitor, document
6. Address root drivers, not just symptoms	Where obesity or central adiposity is present, consider structured weight management (diet, exercise, GLP-1 agents)	Obesity treatment can improve diabetes and sleep apnea simultaneously, reducing net drug count
7. Use shared decision-making	Discuss quality-of-life goals and medication trade-offs directly with the patient	Patients want fewer medications but rely on clinicians to initiate the conversation

Practical Entry Points for Clinicians

• Start small: pick one or two patients per visit day to begin a deprescribing conversation rather than attempting system-wide overhaul.

• Target medications with known age-related metabolism changes first (beta-blockers, anticholinergics) since these carry outsized fall and cognitive risk in older adults.

• Use a "pause and monitor" (drug holiday) approach as a lower-risk intermediate step before full discontinuation, with explicit criteria for restarting if symptoms return.

• Recognize that weaker trial evidence often exists for drugs in polymorbid or frail patients, meaning benefit assumptions from single-disease trials may not transfer directly.

This framework treats polypharmacy not as an inevitable accumulation but as a reversible process — one where regular reassessment of the body's own trajectory, not just symptom-by-symptom prescribing, determines whether medication burden grows or shrinks over time.

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Mykola Iabluchanskyi together with Andriy Yabluchanskiy