Why Everything we’re Doing for Skin, Hair, and Weight May Not Translate Clinically

Dr. Rakshitha Jayaprakash, MD

Dermatologist & Founder of Legacy – Concierge Medicine

• Focused on aesthetic dermatology, regenerative medicine, and holistic longevity science.
• Quantifying inflammatory and metabolic signals across dermatological presentations

Dr. Rakshitha Jayaprakash is a future-focused MD dedicated to the intersection of science-backed beauty and systemic health. Moving beyond traditional “quick fixes,” she specializes in Regenerative Aesthetics, focusing on how to biologically optimize the skin for long-term vitality rather than just temporary change.

Her clinical philosophy centers on the belief that a natural glow is the result of living optimally. By prioritizing longevity and cellular health, Dr. Rakshitha helps patients move away from transactional treatments toward comprehensive anti-aging solutions that empower the skin to function and look younger.

https://www.instagram.com/dr.rakshitha.jayaprakash.md/

Introduction

Clinicians across dermatology and metabolic medicine frequently encounter presentations that appear resistant to otherwise appropriate interventions. These may include androgenetic alopecia, persistent acne, or difficulty altering body composition despite consistent behavioural inputs.

Such cases often create a degree of clinical incongruence—where expected responses are not observed despite apparently adequate inputs.

While traditionally approached within separate domains, emerging human data suggests that quantifiable differences in inflammatory and metabolic markers may distinguish these populations from controls, raising questions about how these signals are interpreted in clinical contexts.

Background: Chronic Low-Grade Inflammation

Chronic low-grade inflammation describes a sustained activation of immune pathways without overt clinical signs of acute inflammation. It is typically characterised by modest but persistent elevations in cytokines such as interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α).

Human studies have associated this inflammatory state with:

• Altered insulin signalling pathways
• Changes in adipose tissue function
• Modulation of hair follicle cycling
• Alterations in cutaneous inflammatory activity

Adipose tissue is now recognised as an endocrine organ capable of secreting adipokines and cytokines that contribute to systemic inflammatory tone (Hotamisligil, 2006).

Inflammatory Signalling in Dermatological Conditions

Several human studies have reported measurable differences in inflammatory markers across dermatological conditions.

In a case–control study of androgenetic alopecia (AGA) (n≈80), serum IL-6 and TNF-α levels were reported to be approximately 20–30% higher than in matched controls (p<0.05) (Huang et al., 2026).

In alopecia areata cohorts (n≈58), IL-6 and TNF-α were also significantly elevated compared to controls, with IL-6 demonstrating a moderate positive correlation with disease severity (r=0.41, p<0.01) (Torkestani et al., 2021).

Histological studies in AGA have additionally described perifollicular inflammatory infiltrates and early fibrotic changes within the follicular microenvironment (Li et al., 2025).

In acne, inflammatory cytokines such as IL-1β and IL-6 have been shown to increase in lesional skin, reflecting activation of innate immune pathways (Kurokawa et al., 2009).

These findings indicate that inflammatory signalling is quantifiably altered across multiple dermatological presentations, although the clinical significance of these differences remains under investigation.

Metabolic Correlates: Insulin Resistance and Adipokines

Inflammatory signalling is also closely associated with metabolic parameters, particularly insulin resistance.

In a cross-sectional study of individuals with AGA (n≈120), insulin resistance measured using HOMA-IR was reported to be approximately 15–25% higher than in controls (p<0.05) (Wu et al., 2023).

The same cohort demonstrated:

• Elevated leptin levels (approximately +20–25%)
• Reduced adiponectin-to-leptin ratios (approximately −15–20%)

These changes are consistent with a pro-inflammatory metabolic profile, which has been observed in association with both adipose tissue dysfunction and systemic inflammatory signalling.

Additional studies have reported higher prevalence of metabolic syndrome components in individuals with inflammatory skin diseases, including acne and psoriasis (Hu et al., 2019).

Laboratory Reference Ranges and Clinical Variability

A commonly encountered clinical observation is the persistence of symptoms despite laboratory values falling within reference intervals.

Reference ranges are statistically derived from population distributions and are designed to identify overt pathology. They do not necessarily reflect tissue-specific functional requirements.

For example, ferritin values may span a wide range within “normal” limits (e.g., 15–300 ng/mL), yet cellular demands—particularly in rapidly proliferating tissues—may not be uniform across this spectrum.

This creates a recurring pattern:

• Biochemical values within reference limits
• Ongoing clinical variability

While widely observed, definitive functional thresholds are not standardised within current literature.

A Conceptual Model: Signal Presence vs Signal Utilisation

Some clinicians have begun to interpret these discrepancies through a distinction between:

Signal Presence

Measured levels of:

• Hormones
• Nutrients
• Growth factors

Signal Utilisation

The capacity of tissues to respond to these signals within a given biological environment.

Within this framework, inflammatory signalling may act as a modifying variable influencing signal utilisation.

For example:

• Cytokine activity has been shown to interfere with insulin receptor signalling pathways (Hotamisligil, 2006)
• Inflammatory microenvironments have been associated with alterations in follicular behaviour

This model does not establish causality but offers a lens through which discordant clinical observations may be interpreted.

Stratified Inflammatory Patterns in Clinical Cohorts

Subgroup analyses in human studies have described variability in inflammatory marker levels across patient populations.

In AGA and related cohorts, observational patterns suggest:

• Lower inflammatory marker groups demonstrate comparatively greater observable changes within study periods
• Intermediate groups show variable responses, often alongside measurable metabolic differences
• Higher inflammatory marker groups demonstrate reduced or inconsistent responses within the same timeframe

These patterns are derived from cohort-level observations and remain non-standardised, with variability in methodology, cut-offs, and endpoints.

Population Variability: South Asian Context

Population-level differences in inflammatory and metabolic markers have also been reported.

Studies involving South Asian cohorts have described:

• Higher baseline IL-6 levels compared to Western populations (approximately 10–20% variation across cohorts)
• Differences in iron metabolism and ferritin distribution
• Increased prevalence of insulin resistance at lower BMI thresholds

These findings highlight the importance of contextual interpretation of biomarkers across populations, although population-specific clinical thresholds are not currently standardised.

Limitations and Uncertainty

Despite increasing quantification of inflammatory and metabolic markers, several limitations remain:

• Many studies involve modest sample sizes
• Cross-sectional designs limit causal inference
• Biomarker variability affects reproducibility
• No single marker reliably represents systemic inflammatory status

Additionally, observed associations may represent:

• Contributing factors
• Secondary responses
• Or non-causal correlations

Professional Context

The growing body of quantified data linking inflammation, metabolism, and dermatological conditions reflects a broader shift toward systems-based thinking.

However, variability in study design, populations, and endpoints limits direct translation into unified clinical frameworks. As such, these findings are best interpreted as contextual signals rather than definitive determinants.

Key Takeaway

Human studies have demonstrated measurable differences in inflammatory and metabolic markers in dermatological conditions, including:

• Elevated IL-6 and TNF-α (approximately 20–30% higher in AGA cohorts vs controls in small case–control studies)
• Increased insulin resistance indices, including HOMA-IR (approximately 15–25% higher in some populations)
• Altered adipokine profiles, including higher leptin and reduced adiponectin-to-leptin ratios

Subgroup analyses suggest variability in inflammatory burden across patients, which may be associated with differences in observed clinical patterns.

However, these findings remain associative rather than predictive, and current evidence does not establish standardised thresholds or causal pathways.

References (Harvard Style)

• Hotamisligil, G.S. (2006) ‘Inflammation and metabolic disorders’, Nature, 444(7121), pp. 860–867.
• Wu, Y.F. et al. (2023) ‘The association of serum adipokines, insulin resistance and vitamin D status in androgenetic alopecia’, Clinical, Cosmetic and Investigational Dermatology, 16, pp. 123–131.
• Torkestani, S. et al. (2021) ‘Evaluation of serum IL-6 and TNF-α in alopecia areata patients’, Biomedical Research and Therapy, 8(2), pp. 4123–4130.
• Li, L. et al. (2025) ‘The role of fibrosis in androgenetic alopecia’, Skin Appendage Disorders.
• Kurokawa, I. et al. (2009) ‘New developments in acne pathogenesis and treatment’, Experimental Dermatology, 18(10), pp. 821–832.
• Hu, Y. et al. (2019) ‘Metabolic syndrome and skin diseases’, Frontiers in Endocrinology, 10, p. 788.
• Huang, F. et al. (2026) ‘Inflammatory markers in androgenetic alopecia’, Journal of Dermatological Science.

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