AaharIQ Feature Guide: How the App Actually Works

• Diabetes Filter: Evaluates glycaemic load per serving, identifies hidden sugars (maltodextrin, dextrose, rice syrup, fruit juice concentrate — all raise blood glucose equivalently to sucrose), flags products with >6g added sugar per serving, evaluates carbohydrate quality (whole grain vs refined), and provides a "diabetes safety score" of 1–10.

• Hypertension / Sodium Filter: Calculates sodium per 100g and per serving, benchmarks against WHO's 2,000mg/day maximum, flags products in the "high sodium" (>600mg/100g) and "very high sodium" (>1,500mg/100g) categories, identifies hidden sodium sources (sodium benzoate, sodium citrate, monosodium glutamate — additives that contribute to sodium load beyond labelled "sodium as salt" figures), and evaluates potassium content (protective against sodium's BP effects).

• Thyroid Filter: Identifies goitrogens (isothiocyanates from cruciferous vegetables, cassava-based ingredients), evaluates iodine content and iodised salt usage, identifies soy isoflavones that may interfere with levothyroxine absorption in sensitive patients, flags products with high fluoride content (excess fluoride suppresses thyroid in iodine-deficient individuals), and checks for selenium content (required for T4→T3 conversion).

• PCOS Filter: Evaluates glycaemic load, identifies testosterone-precursor additives, flags dairy-heavy products for patients on dairy-reduction protocols, evaluates insulin index (beyond glycaemic index — particularly relevant for PCOS), identifies anti-androgenic food components, and evaluates omega-6:omega-3 ratio contribution per serving.

• Kidney Health Filter (CKD/Nephropathy): Evaluates phosphorus content, identifies phosphate food additives (E338–E452 series — bioavailable phosphate additives that are more absorbed than naturally occurring food phosphorus), flags high-potassium products for CKD stage 3b+, evaluates sodium content, assesses protein per serving against CKD protein restriction thresholds, and identifies oxalate-rich products (relevant for kidney stone formers).

• Gout / Uric Acid Filter: Identifies HFCS and crystalline fructose content (primary modern gout trigger), evaluates purine contribution from animal protein-containing products, flags beer and alcohol in cooking ingredients, evaluates sodium content (diuretics prescribed for associated hypertension further raise uric acid).

• Bone Health Filter: Tracks calcium content per serving, identifies Vitamin D3 fortification (distinguishing D2 vs D3 — D3 is more bioavailable), flags phosphoric acid (bone calcium-leaching — found in colas), evaluates sodium (urinary calcium loss driver), identifies oxalate-calcium ratio in plant-based products (oxalates reduce calcium absorption), and flags products interfering with calcium absorption (excess zinc or iron in same product competes with calcium).

• Inflammation / Autoimmune Filter: Identifies emulsifiers with gut-disrupting evidence (carrageenan/E407, CMC/E466, polysorbate-80/E433), flags titanium dioxide (E171 — banned in EU; still permitted in India), evaluates artificial dye content (Red 40, Yellow 5/6 — associated with hyperactivity and immune activation in sensitive individuals), identifies trans fats, evaluates omega-6 contribution per serving, and flags HFCS and excess fructose.

• AI Meal Analyser: Photograph a home-cooked meal; AaharIQ estimates nutritional composition, glycaemic load, and disease filter evaluation for non-packaged food using computer vision and Indian recipe database.

• Blood Glucose Correlator: Connect with continuous glucose monitors (CGM) via API; AaharIQ will correlate which scanned products created the highest glucose spikes in your personal data — creating truly personalised low-GI guidance.

• FSSAI Recall Alerts: Real-time push notifications when FSSAI issues a recall or enforcement notice for a product you have previously scanned or that matches your profile.

• Family Health Profiles: One account managing multiple family members with different health conditions — grandfather with CKD, mother with PCOS, child with egg allergy — each family scan gets multi-profile evaluation simultaneously.

• Pharmacist Integration: Cross-reference scanned product ingredients against prescription medications for food-drug interactions — grapefruit and statins, vitamin K and warfarin, St John's Wort and antidepressants, and India-specific interactions with ayurvedic supplements and common Indian foods.

• Restaurant Menu Scanner: Photograph restaurant menus and get AaharIQ health filter evaluation of dishes — extending the safe eating guidance beyond packaged food to dining out.

AaharIQ's health filter recommendations are built on: ICMR dietary reference values and disease guidelines, FSSAI standards and notifications, WHO Global Action Plan for NCDs, Cochrane systematic reviews and meta-analyses for each disease filter criterion, PREDIMED, UKPDS, NHANES, NFHS-3/4/5, ICMR-INDIAB, and IDF Atlas data for India-specific epidemiological context. All health claims in AaharIQ's disease filters are referenced to published peer-reviewed literature with no assumptions made from unvalidated sources. The AaharIQ team commits to updating filter criteria within 90 days of major guideline revisions from ICMR, ADA, ESC, NICE, and WHO.

Getting the most out of AaharIQ's disease-specific filters depends entirely on how completely a user's health profile is set up during onboarding, since every scan's evaluation runs against this profile rather than producing a generic, one-size-fits-all result. The setup process starts with selecting any diagnosed conditions relevant to the available filters — diabetes, hypertension, thyroid disorders, PCOS, CKD, gout, coeliac disease, IBS, and the other categories covered in this guide's filter breakdown — followed by entering current medications, since several filters, including the kidney and gout filters, adjust their thresholds based on medication-specific factors. Known allergies and intolerances feed directly into the ingredient analyser's allergen-flagging logic, and dietary preferences (vegetarian, vegan, Jain, or specific regional dietary patterns) refine which products the comparison mode surfaces as genuinely relevant alternatives. A profile left incomplete doesn't break the app — scans still return general nutritional and ingredient information — but it does mean the specific, clinically tailored disease-filter evaluations this guide describes won't activate for conditions that were never entered, which is why revisiting and updating the health profile after any new diagnosis, medication change, or doctor-recommended dietary adjustment keeps the app's guidance aligned with current, accurate health status rather than an outdated snapshot from initial sign-up. A useful habit is treating the health profile the same way one would treat a doctor's intake form — reviewing and updating it at least once every few months, or immediately after any appointment that changes a diagnosis or prescription, rather than only touching it once during initial setup and forgetting about it afterward. Five minutes of setup today saves confusion at every future scan. A small investment with a real return. Do it before the first scan, not after. Simple, quick, worthwhile.

The 1-10 disease safety score referenced throughout this guide's filter breakdown isn't a single flat number pulled from one data point — it's a weighted aggregate combining multiple factors specific to each disease filter's clinical priorities. For the diabetes filter, for instance, glycaemic load per serving, added sugar content, and carbohydrate quality (whole grain versus refined) are each weighted according to their relative importance in blood glucose management research, then combined into the single score displayed after a scan, rather than treating all contributing factors as equally significant. This weighting differs meaningfully across filters — the kidney filter weights phosphorus and phosphate additive content more heavily than the diabetes filter would, reflecting CKD management's specific clinical priorities, while the gout filter weights purine and fructose content most heavily. Understanding this weighted-aggregate structure matters for interpreting a score correctly: two products can receive a similar overall score while differing considerably in which specific factor is driving that score down, which is why the score displayed alongside a scan result is paired with the specific factor breakdown underneath it, rather than the number being presented in isolation without the reasoning behind it. Reading past the headline number to the underlying breakdown is where the score becomes genuinely actionable rather than just a quick traffic-light judgement. Two products with the same number can still call for two different decisions depending on which specific factor is driving the score. Numbers with context beat numbers alone. Simple to read, rigorous underneath. Clarity by design.

Given India's enormous and constantly evolving packaged food market, no barcode database — AaharIQ's included — will ever achieve complete, real-time coverage of every regional brand, small manufacturer, and newly launched product on Indian shelves. When a scan doesn't return a match, AaharIQ prompts the user to photograph the product's ingredient list and nutrition panel directly, allowing the ingredient analyser and disease filters to still run against that specific product even without a pre-existing database entry, rather than leaving the user with no information at all. This scanned information also feeds back into expanding AaharIQ's own India-specific product database over time, meaning frequent users of less common or regional products are, through ordinary use of the app, directly contributing to closing exactly the kind of database coverage gap that structurally limits internationally developed apps retrofitted for the Indian market, as covered in this site's dedicated comparison piece on why globally built scanning apps underperform for Indian consumers. This approach treats database completeness as an ongoing, community-reinforced process specific to the Indian market, rather than a fixed, one-time catalogue that inevitably falls behind the pace of new product launches. This same photograph-based evaluation is particularly useful for the small, regional, or artisanal food brands common across different Indian states, which large international databases have historically been slowest to include. Every contribution helps the next shopper too. That's the whole point of a community-reinforced database. Small contributions, real collective value. Worth doing.

Given how sensitive a detailed health profile — diagnosed conditions, medications, allergies — genuinely is, it's worth addressing directly how this information is used within the app rather than leaving privacy practice as an assumed detail. Health profile data exists specifically to power the personalised disease-filter evaluations this guide describes throughout — it isn't sold to or shared with food manufacturers, insurers, or third-party advertisers, consistent with the same no-manufacturer-payment, no-product-placement commitment already covered in this guide's data sources section. This separation matters specifically because a scanning app that both holds detailed health data and accepts manufacturer payment for product visibility would face a structural conflict of interest — the incentive to surface a paying brand's products favourably would sit in direct tension with the incentive to give an accurate, personalised health evaluation based on that same user's actual medical profile. Keeping the health-data and business-model layers entirely separate is precisely what allows AaharIQ's disease filter evaluations to remain something a user can trust reflects their actual health needs rather than a commercial relationship influencing what gets recommended. Trust, once established this way, is worth protecting deliberately. Health data should serve the user first, always. That commitment is worth restating clearly. Simple, but not to be taken for granted.